energies Article A Simplified Techno-Economic Analysis for Sophorolipid Production in a Solid-State Fermentation Process María Martínez 1,2 , Alejandra Rodríguez 3 , Teresa Gea 3, * and Xavier Font 3 1 Escuela de Ingeniería Bioquímica, Facultad de Ingeniería, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2085, Valparaíso 2362803, Chile; maria.martinez.v@mail.pucv.cl 2 Centro de Biotecnología de Sistemas, Universidad Andrés Bello (CSB-UNAB), Fernández Concha 700—Las Condes, Santiago 7591538, Chile 3 Composting Research Group, Department of Chemical, Biological and Environmental Engineering, Escola d’Enginyeria, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain; alejandra.rodgt@gmail.com (A.R.); xavier.font@uab.cat (X.F.) * Correspondence: teresa.gea@uab.cat; Tel.: +34-935812694 Abstract: Sophorolipids (SLs) are microbial biosurfactants with an important role in industry and a continuously growing market. This research addresses the use of sustainable resources as feedstock for bioproducts. Winterization oil cake (WOC) and molasses are suitable substrates for SLs via solid-state fermentation (SSF). The model proposed herein was established for annually processing 750 t of WOC and comparing three support materials: wheat straw (WS), rice husk (RH), and coconut fiber (CF). Production capacity ranged 325–414 t of SLs per year. Unit Production Cost was 5.1, 5.7, and 6.9 USD/kg SL for WS, RH, and CF production models, respectively, and was slightly lower with other substrates. Financial parameters were CAPEX 6.7 MM USD and OPEX 1.9 MM USD/y, with a NPV, IRR and payback time of 6.4 MM USD, 31% and 3.2 y, respectively. SLs recovery from the solid matrix was the major contributor to operating costs, while fermentation equipment shaped capital Citation: Martínez, M.; Rodríguez, A.; costs. Results show that the physical properties (bulk density, WHC) of substrates and supports Gea, T.; Font, X. A Simplified Techno-Economic Analysis for define process costs beyond substrate purchase costs and process yields in SSF systems. To our Sophorolipid Production in a knowledge, this is the first attempt to model SLs production via SSF at full scale for the economic Solid-State Fermentation Process. valuation of the SSF process. Energies 2022, 15, 4077. https:// doi.org/10.3390/en15114077 Keywords: solid-state fermentation; sophorolipids; waste; biosurfactant; techno-economic analysis Academic Editors: Solange I. Mussatto, Giuliano Dragone and Andrzej L. Wasiak 1. Introduction Received: 9 April 2022 New global trends point to the use of sustainable products and increasing circularity in Accepted: 26 May 2022 production processes. In this way, circular economy and bioeconomy are being promoted Published: 1 June 2022 by most governments. These two strategies overlap in a circular bioeconomy strategy when Publisher’s Note: MDPI stays neutral a product is bio-based and produced from organic waste or side streams [1]. Surfactants, with regard to jurisdictional claims in usually organic compounds, amphiphilic and with properties to reduce the surface tension published maps and institutional affil- between two phases, are extensively used in household products and industrial applica- iations. tions. Surfactants are generally petrol-based and produced through synthetic chemical processes; they have a low biodegradability [2], and some of them, or their degradation products, are toxic [3]. The surfactants market size is projected to reach USD 52.4 billion by 2025, up from USD 42.1 billion in 2020, at a compound annual growth rate (CAGR) Copyright: © 2022 by the authors. of 4.5% [4]. This makes surfactants a target product for circular bioeconomy practices; Licensee MDPI, Basel, Switzerland. however, economic feasibility must be proved. Biological origin surfactants are known as This article is an open access article biosurfactants (BS). The global BS market was estimated at USD 4.20 billion in 2017 and is distributed under the terms and conditions of the Creative Commons projected to reach USD 5.52 billion by 2022, at a CAGR of 5.6% from 2017 to 2022 [5]. The Attribution (CC BY) license (https:// BS market is boosted by the growing awareness that they are biodegradable compounds creativecommons.org/licenses/by/ and, as such, are environmentally friendly and non-toxic, with antimicrobial and antifungal 4.0/). properties, among others [6]. Energies 2022, 15, 4077. https://doi.org/10.3390/en15114077 https://www.mdpi.com/journal/energies Energies 2022, 15, 4077 2 of 23 BS also are amphiphilic molecules, but in this case, the hydrophilic head corresponds to a sugar, an ester, a hydroxyl, a phosphate, or a carboxylate group. The hydrophobic tail is constituted by proteins, peptides, or fatty acids of 10 to 18 carbons [7]. BS can be of low molecular weight (glycolipids, phospholipids, and lipopeptides) with high superficial tension reduction power; or high molecular weight (proteins, lipoproteins, polysaccharides, or lipopolysaccharides) with higher emulsifying properties [7], reporting similar emulsification properties to the non-ionic commercial surfactant Triton X-100 [8]. Sophorolipids (SLs), glycolipids produced by non-pathogenic yeast [9], are one of the most studied due to their characteristics and applications. These compounds have presented promising results for use as food emulsifiers [10], germicides [11], anticancer agents [12], in several environmental applications [13,14], as antimicrobial agents to formulate oral hygiene products [15] and cosmetics or pharmaceutics [11], as cleaning products [16], and in food formulation [17]. Specifically, the global SL market was valued at USD 375 million in 2019 and is projected to reach USD 547 million by 2027; it is expected to grow at a CAGR of 5.0% from 2020 to 2027 [18]. From this information, it can be assumed that the SL market only accounts for nearly 8.0% of the BS global market and is growing at a similar rate. The global BS production was estimated at 477 kilotons by 2018 [19], a higher value than the 462-kilotons by 2020 reported elsewhere [20]. In this report, household detergents and personal care applications were the larger application segments, with 154 and 51 kilotons, respectively. Europe was the largest regional market with a consumption of 179 kilotons. Even though de novo sophorolipid synthesis is possible using the yeast Starmerella bombicola, SL production is performed by the addition to the fermentation process of a hydrophilic and a hydrophobic carbon source. Generally, SLs are produced by submerged fermentation (SmF), using the yeast Starmerella bombicola and pure substrates such as glucose [21], combining glucose with oleic acid [22], or using several different organic waste streams as carbon sources [13]. The cost of substrates dictates the minimum production costs. In addition, when first-generation substrates are used, the related environmental impacts greatly affect sus- tainability, so research is focused on using second to fourth-generation feedstock [23]. Recently, Solid-State Fermentation (SSF) has been noticed as a technology suitable for a circular bioeconomy, as the use of solid substrates allows the valorization of organic solid wastes and by-products [24], which are inexpensive solid substrates, such as oil cakes from an edible oil refinery [9] or mango kernel [25]. SSF is characterized as a process in which microorganisms grow in the near absence of free water, demanding less energy for sterilization; it is also less susceptible to microbial contamination and to substrate inhibition, and ultimately allows higher productivity for many enzymes [26], with the subsequently reduced operational costs, which arise mainly from mixing and heating. However, draw- backs related to instrumentation and control, poor homogeneity, and energy and mass transfer affect the process yield, and a more complex downstream process compared with SmF should be considered [27]. Moreover, the solids derived from SSF might be used as a soil amendment or for soil bioremediation [28]. Recently, we have demonstrated the feasibility of producing SLs from oil cake and molasses from the food industry by SSF at a representative scale [29]. Also, the system can admit different lignocellulosic materials as supports [30]. This is a significant advantage due to the low cost of the lignocellulosic materials, such as current agricultural residues, and provides a revalorization option. The surface and structure of the support materials are key parameters to favor the immobilization of microorganisms. On the one hand, the roughness of the surface facilitates the fixation of microorganisms on the support. On the other hand, the porosity increases contact surface, thus increasing the potential surface available for immobilization. Porosity is usually greater in inert and synthetic materials (e.g., polyurethane foam) than in biomass supports, their pore size being of a higher diame- ter; however these materials generally present smoother surfaces. Agricultural residues have the structure and roughness that favor the fixation of microorganisms into supports Energies 2022, 15, 4077 3 of 23 that, in combination with optimal conditions, will allow higher production yields compared with synthetic supports. Indeed, Kilonzo et al. [31] studied the immobilization of different strains of Saccharomyces cerevisiae, reporting that cotton showed the highest efficiency of cell absorption compared with synthetic materials (polyurethane foam, nylon, polyester). Similarly, Rodríguez et al. [30] obtained higher SLs production using lignocellulosic materi- als than with polyurethane foam. Because the availability of lignocellulosic materials is seasonal and depends on the geographical location, SLs production using different support materials has been assessed [30]. Several published studies analyze the cost of producing SLs under SmF. According to Van Bogaert et al. [32], production price strongly depends on the substrates used and the production scale, ranging between 2 and 5 USD/kg. Ashby et al. [33] simulated a high production volume (90.7 million kg production process) with a yield of 100 g/L using S. bombicola, with glucose and sunflower oil as substrates, achieving a production cost of 2.95 USD/kg. More recently, Wang et al. [34] have reported a techno-economic study for the production of SL in crystal and syrup presentation forms, from hydrolyzed food waste and comparing different sources of equipment costs, with the production cost ranging from 19.65 USD/kg to 16.45 USD/kg. The cost can be reduced to 10 USD/kg with an integrated bioprocess design [23]. However, no techno-economic studies have been reported to produce SLs through SSF. Downstream processing for SL production at a commercial scale is based on SmF. Here, easy recovery of biosurfactants can be achieved by centrifugation and spray drying, thus removing the majority of the water and the remaining cells from the product [33]. Van Bogaert et al. [32] indicate that SL can be separated from the fermentation media by centrifugation or decantation, with further elimination of water and impurities by the addition of polyhydric alcohols followed by distillation. Also, different strategies have been suggested for integrated bioprocessing [35]. As has been pointed out above, SSF opens the production of SL to the use of organic solid wastes and by-products as substrates. However, in this case, downstream processing for SL is rather different, mainly due to the fermentation matrix (a solid). Different methodologies can be used to optimize production at lab/pilot scale, such as experimental design [36], neural networks [37], and recently, machine learning [38]. Also, for SmF, several reports on the modeling of the economics of full-scale production and related environmental impact [39] are available. However, there are no reports dealing with the modeling of full-scale processing via SSF nor the environmental impact of the process. Therefore, modeling the industrial process is essential to study and estimate the costs and the environmental impacts of SL production through SSF on a small scale. In this way, it is possible to establish whether this process for SL production is an economical alternative to substitute the chemically synthesized surfactants by biosurfactants based on a circular bioeconomy strategy and whether its cost is competitive with SL produced by SmF. The aim of this work is to perform techno-economic analysis of a small scale sophorolipid production plant through the valorization of by-products from the food industry (win- terization oil cake, molasses) via solid-state fermentation, comparing three supporting biomaterials (wheat straw, rice husk, coconut fiber) and based on our previous research [30]. Plant size was set to process 750 t WOC/y. The production capacity was designed to be implemented related to the production of an existing edible oil processing facility intended to valorize its winterization oil cake waste. Obtained results were compared to the use of raw materials as substrates (soybean oil, oleic acid, and glucose) and to the use of other by- products (sunflower oil cake, frying oil, and mango-kernel fat). Techno-economic analysis also includes the comparison between a brand-new facility and implementing the process in an existing edible oil refinery (when WOC is used as substrate and in both cases with the same SSF process configuration), and the use of SmF instead of SSF Energies 2022, 15, 4077 4 of 23 2. Materials and Methods 2.1. Base Scenario The base scenario was based on previous work performed at different scales, with reactor volumes of 500 mL [9], and 22 and 100 L [29]. These studies demonstrated the feasibility of the production of SL through SSF at bench and pilot-scale, using winterization oil cake (WOC) and molasses (MOL) as substrates, wheat straw (WS) as the support material to provide porosity and resistance to compaction, and S. bombicola ATCC 22214 (American Type Culture Collection, Manassas, VA, USA) as the SL-producing yeast. Under these conditions, the productivity is 0.026 g SL/g dry matter d, and the obtained production yield is 0.72 g SL/g fat (corresponding to 0.26 g SL/g WOC). Based on the results obtained for 100 L reactors, we considered a scale-up factor and assumed to achieve 85% of the SL yield obtained in our previous works, as a conservative value in the simulation [29]. In this base model, SLs are extracted with ethyl acetate, the organic extract is filtrated, and the solvent is later recovered by evaporation. The crude extract is finally dried in a drum dryer. SL extract, the main product, was considered a combination of 80% SL and 20% impurities, as a conservative value, and took into account that a lower impurity content would require further downstream processing. Depending on the final application, this crude SL extract can be further processed to obtain purer SL forms, such as crystals. The base scenario considers that the new SL production plant is dedicated to process WOC produced by a single oil refinery, according to data from local refineries in Spain. This capacity can be increased up to 5× times by including other WOC generating industries near the original facility location. Transportation distances should be considered due to the characteristics of the supporting material as organic biomass. However, they are not included in this preliminary model. In Table 1, raw material composition and the corresponding annual flows are presented. Table 1. Raw material composition and annual flows used in the base model. Material % w/w t/y Winterization Oil Cake 750 Carbohydrates 0% – Fats 67.5% 506 Perlite 32.5% 244 Molasses 188 Carbohydrates 65% 122 Fats 0% – Others 1 35% 66 1 Insoluble carbohydrates, proteins, and ash. 2.2. SL Production Process The SL production process was based on processing 750 t/y WOC at a ratio of WOC:MOL 4:1 (on a mass basis). Plant scale was selected based on WOC production in several facilities in Spain. The process includes five stages: (1) material sterilization, (2) fermentation, (3) SL extraction, (4) SL recovery, and (5) waste treatment. The flow diagram for SL production is presented in Figure 1. Inoculum preparation is not included in the diagram but is considered in the cost analyses. 2.2.1. Material Sterilization Material sterilization was set on batch mode lasting one day. In the model, WOC (S-103), MOL (S-104), and WS (S-105) are stored in separate bins before being sterilized. To avoid contamination due to fungal spores potentially present in agro-industrial lignocellu- losic wastes, the supporting material is sterilized twice before being mixed and sterilized a third time with the rest of the materials. Batch time comprises three sterilization cycles, each one including 45 min of heating at 121 ◦ C giving a sterilization period of 17 h in total Energies 2022, 15, 4077 5 of 23 Energies 2022, 15, x FOR PEER REVIEW 5 of 23 considering the loading and unloading of the mixture. Two sterilization lines and 1-cycle per day are proposed with 75% of usage for each piece of equipment. Figure 1. Flow Figure 1. Flow diagram diagram for for SL SL production production from from WOC WOC by by solid-state solid-state fermentation fermentation and and by by organic organic solvent solvent recovery. recovery.Nomenclature: Nomenclature:WOC: WOC:winterization oiloil winterization cake, MOL: cake, MOL: molasses, WS:WS: molasses, wheat straw, wheat EA: straw, EA: Ethyl Ethyl acetate, acetate, SL: sophorolipids. SL: sophorolipids. 2.2.2. 2.2.1. Fermentation Material Sterilization SSF was set Material on batch mode sterilization was set lasting five mode on batch days (time lastingforonemaximum productivity). day. In the model, WOC With (S- an operational availability of 330 d/y (7920 h/y), two lines comprising 103),3 MOL (S-104), and WS (S-105) are stored in separate bins before being sterilized. To five fermenters each (5 m ) for avoid treating 750 due contamination t/y of to WOC fungalwere considered spores potentiallybased on a setup present time of 1 day lignocel- in agro-industrial between batches for loading and unloading the fermented biomass and cleaning the equipment. lulosic wastes, the supporting material is sterilized twice before being mixed and steri- This gave 55 fermentation batches per year. The bioreactor system was set in the design by lized a third time with the rest of the materials. Batch time comprises three sterilization adapting the parameters indicated in [30] using a diameter/height ratio of 0.6 in SS304 and cycles, each one including 45 min of heating at 121 °C giving a sterilization period of 17 h working at 75% of the total capacity for gas transfer and agitation procedures. After the in total considering the loading and unloading of the mixture. Two sterilization lines and sterilized material (S-106) was transferred to the fermentation tank, S. bombicola was added 1-cycle per day are proposed with 75% of usage for each piece of equipment. as the inoculum (S-102) at a ratio of 1:9 (w/w). Fermentation conditions were set at 30 ◦ C, 27 kg/h of continuous supply of humidified air (45% HR), and with intermittent agitation 2.2.2. Fermentation (every two days, [9]). SSF was set on batch mode lasting five days (time for maximum productivity). With an operational 2.2.3. SL Extraction availability of 330 d/y (7920 h/y), two lines comprising five fermenters each (5 m3The ) forextraction treating 750 t/y of stage was WOC were considered designed based on amode. in a semicontinuous setup time Threeofoperation 1 day between lines batches were for loading considered and unloading for extraction the fermented and further recuperationbiomassdue and cleaning the to processing equipment. volume, opera- This gave tional 55 fermentation flexibility, and/or equipmentbatches per year. The bioreactor system was set in the design capacity. by adapting the parameters indicated The model comprises a set of extractors in [30] using coupled a diameter/height to screw and press ratiofilters of 0.6working in SS304 and working at 75% of the total capacity for gas transfer and on a staggered mode in three shifts per day. In this stage, fermented biomass (S-107) agitation procedures. After is transferred to the extraction units after each fermentation batch is completed. SLs was the sterilized material (S-106) was transferred to the fermentation tank, S. bombicola are added as the recovered inoculum in ethyl acetate (S-102) at a ratio of to fed continuously 1:9the (w/w). Fermentation process (S-108) by conditions were set direct application andat 30 °C, 27of washing kg/hthe of continuous fermented supplyFermented biomass. of humidified air (45% biomass HR), and is extracted withethyl using intermittent acetate agitation in (every two a liquid-to-solid days, ratio of [9]). 5:1 (v/w) in a single-extraction process lasting 15 min at room temperature. After extraction, a decanting phase is used for gravity separation of the 2.2.3. SL Extraction maximum amount of the extract (S-110) before the biomass enters the screw press. Through this process, The extraction70% ofstage the ethyl acetate isinrecovered was designed initially mode. a semicontinuous while Three decanting the solids. operation lines The wereslurry (S-109)for considered is extraction further separated by screw and further pressingdue recuperation intotothe SL extract processing (S-112)oper- volume, and residual biomass (S-111). After ational flexibility, and/or equipment capacity. extraction, the SL extract recovered by decanting and pressing The is modelcombined comprises and apassed set of through extractorsa coupled press filter for suspended to screw and press solids filters removal working (4.0% in the extract). The clean extract is then derived on a staggered mode in three shifts per day. In this stage, fermented biomass to the SL recovery stage,(S-107) and theis residual biomass is sent for treatment. The models consider costs transferred to the extraction units after each fermentation batch is completed. SLs are re- for spent solids managed through covered anaerobic digestion in ethyl acetate fed in an existing industrial continuously facility to the process in theby (S-108) region, directsince biomethane application and production potential has been confirmed in the range of biowaste washing of the fermented biomass. Fermented biomass is extracted using ethyl acetate in [29]. a liquid-to-solid ratio of 5:1 (v/w) in a single-extraction process lasting 15 min at room temperature. After extraction, a decanting phase is used for gravity separation of the Energies 2022, 15, 4077 6 of 23 2.2.4. SL Recovery This process was designed in a continuous mode. The liquid fraction containing the SLs is evaporated, and the paste is dried to obtain SL crystals. For this, a single-stage evaporator is loaded with SL extract (S-113) at room temperature in which the solvent (ethyl acetate) is evaporated by heat transfer using vapor at 152 ◦ C. The SL paste derived from the evaporator (43% solids) (S-114) is dried in a rotavapor at 70 ◦ C to obtain the final product of SL (>99.0% of solids) (S-115) [40]. Recovery of the solvent from the evaporator and the drum dryer is carried out in a condenser at 20 ◦ C using chilled water. Vapor fractions are condensed and 99.9% of the solvent is recovered (S-117), while the remainder is lost to the atmosphere (S-116). The recovered solvent (95% of total solvent used in S-108) is reused within the process. Solid waste treatment: Solid residues after SL extraction are used as a material for biogas production in a co-digestion plant. Biogas production is estimated based on experi- mental results [29]. In this model, 5% of the solvent is lost, embedded in the solid matrix of the waste material, despite the support material. This assumption should be validated in further experiments. 2.3. Comparison Scenarios 2.3.1. Support Material Comparison Having a seasonally produced material is a major inconvenience for an annual oper- ating facility, mainly due to the costs of accumulating the biomass (and also sometimes pretreating it) before processing, and generally also involves oversizing the plant if biomass accumulation is limited because of material degradation. By combining different support materials throughout the year, the processing plant can be fitted properly to maintain a steady SL production on a year-round basis. This uncertainty of the seasonal and geograph- ical production of agricultural wastes was assessed by evaluating two alternative supports to WS. Specifically, rice husk (RH) and coconut fiber (CF) were selected according to [30]. SL yields for RH and CF were 0.22 and 0.28 g SL/g WOC, respectively. In these scenarios, water-holding capacity (WHC) and bulk density were utilized to calculate water addition for each case to obtain the final processing volumes. Bulk density was experimentally determined in previous work, with a value of 35.2 kg/m3 for WS, 117.6 kg/m3 for RH, and 54.4 kg/m3 for CF [30]. 2.3.2. Facility Investment Comparison To assess the effect of investment in a new brand facility, the base scenario will be compared to a scenario where the process plant is located inside the edible oil refinery, producing 750 t/y of WOC. 2.3.3. Substrate Comparison Experimental data regarding SL production using frying oil, oleic acid, and glucose as substrates were gathered from previous studies [41] and adapted to the proposed scenario. Data for mango kernel, and sunflower oil cake and soybean oil were taken from [12,24], respectively. These authors also provided a comparison point for raw materials (oleic acid and glucose). All these processes were simulated under the SSF scenario as indicated in each article and most of the technical parameters described above were applied, with minor modifications due to the physical parameters of the substrates. For comparison, all scenarios were evaluated at the same capacity production of 384 t of SL per year, as this is the capacity value for our novel SL production model. It should be mentioned that even when processes were simulated using the informa- tion proposed by the authors in each evaluated article, process conditions could not be optimized for industrial production and the results obtained here should only be taken as a reference. Energies 2022, 15, 4077 7 of 23 2.3.4. SL Extraction Comparison Although the base model SL extraction was based on a single-step ethyl acetate extraction, a different extraction method (a two-step extraction using methanol and ethyl acetate) was evaluated, as proposed in [12]. 2.3.5. Fermentation Technology Comparison Scenario For comparison purposes a scenario including an SmF process was included. In this case, data for SmF and equivalent SSF process were obtained from [12] using soybean oil (SO) and sunflower oil cake (SOC) and compared to our base scenario. 2.4. Technical Considerations All technical considerations made were based on experimental results and the simula- tion parameters obtained by the SuperPro Designer software v10.2 (Intelligent Inc., Scotch Plains, NJ, USA, [42]). All the scenarios and the process flowsheet were carried out using SuperPro Designer with the objective of generating mass and energy balances from which the requirements for consumables, utilities, and energy needs were calculated. Moreover, this software uses an internal database of industrial equipment with its commercial costs and characteristics. Through engineering parameters, previous bench (0.5 L [9] and pilot-scale (20 and 100 L [29,30]) results were upscaled in a Class 5 analysis, aiming as the first approach in terms of investment and operational costs. Moreover, sensitivity analysis was carried out to highlight critical variables for the future improvement of this preliminary model. For plant sizing, SL production per added WOC (kg SL/kg WOC) was considered using data reported for the three supports used [30]. Because of bulk density and the WHC of the supports, yield in dry mass or in volume can be misleading so, to simplify sensitivity analysis, the SL yield was considered in terms of the fermented material. Another point considered was that, even when productivity results were reported on the 7th day of fermentation, similar results were obtained on the 5th day [30], reducing the operational costs considerably (up to 20%) when initial simulations were made. 2.5. Cost–Benefit Analysis Cost–benefit analysis (CBA) was carried out considering the scheme proposed as a completely new asset because there is no pre-existing infrastructure similar to that proposed. Equipment depreciation, price, and technical contingencies were also considered. Equipment and material inputs/outputs were scaled up, and cost estimation was performed using the same simulation software as indicated in Section 2.1. 2.5.1. Cost Estimations Equipment costs were based on in-house data, information from equipment suppliers, and historical equipment costs. Prices were calculated using the Chemical Engineering Plant Cost Index (CEPCI), when needed, to convert equipment prices to the year 2021 by using Equation (1). In some cases, prices were determined using the SuperPro Designer software, which scales up equipment purchase costs by using a relationship for equipment capacities, as shown in Equation (2). The acquisition of land was not considered as part of the capital costs. An α value of 0.6 was considered, which has been indicated as the six-tenths rule giving an approximate cost within plus or minus 20% [43].   CEPCI2 Cost2 = Cost1 · (1) CEPCI1  α Size2 Cost2 = Cost1 · (2) Size1 Installation and maintenance factors were 30% and 10% of the equipment purchase cost, while unlisted equipment accounted for 25% of the total purchase cost. We also Energies 2022, 15, 4077 8 of 23 considered operational costs, such as raw materials, labor, consumables (equipment parts, replaced items, fungibles, and chemicals), laboratory (off-line analysis, quality control, and quality assurance), utilities (heating and cooling, as well as electricity) and equipment- dependent expenses (depreciation of the fixed capital investment, maintenance of equip- ment, insurance, taxes). Facility expenses and depreciation (linear) were 6 and 5% of direct fixed cost (DFC), respectively. Also, a miscellaneous item (insurance, local taxes, and factory expenses) was included, accounting for 8% DFC. The start-up and validation cost was 5% DFC. The product failure rate was considered 0%, while advertising, selling expenses, and royalties were not included at this point. All these considerations were taken from [43,44], relating to a plant with technology uncertainty at a large scale. The plant was estimated over a 10-year lifetime, including a one-year construction and start-up phase. Labor costs were calculated based on a 24 h workday, 330 days per year (94% of the total time available per year to account for any downtime for repairs, etc., for a total of 7920 h/y). Plant processing lines included five operators (three operators for steril- ization/fermentation, and two operators for SL extraction/recovery), two supervisors (availability 50% of the hours), and one plant manager (availability 25% of the hours) available for the processing operation execution, control, and administration. Salary for the operators was set at the minimum wage of 1238 USD/mo for Spain (BOE, 2018); super- visors and plant manager salaries were set at 1.25× and 1.5× times the minimum wage, respectively. Laboratory/Quality Control/Quality Assurance associated costs were set at 15% of the labor cost. Labor and Quality Assurance costs were assigned equally to the four processing stages. The utilities electricity, steam, and process water were considered during plant pro- cessing. For electricity, electric power for unlisted equipment and general load accounted for 5 and 15% of the total consumption, respectively. Consumables were mainly replaceable parts (accounting for 10% of the respective equipment cost) and solvent replacement for that lost during the SL extraction and recovery stages. Table 2 presents prices for utilities and main raw materials used in the proposed plant (VAT on purchased material and con- sumable costs are included). For waste streams, solid residue treatment through anaerobic digestion was set at the cost of 55 USD/t [45]. Energies 2022, 15, 4077 9 of 23 Table 2. Prices for utilities, main raw materials, and products in the presented scenarios. Parameter Unit Value Used in the Model Utilities Standard power USD/kWh 0.087 1 Steam USD/t 12.0 2 Cooling water USD/t 0.05 2 Chilled water USD/t 0.4 2 Raw material WOC USD/t 58 3 MOL USD/t 167 3 Frying oil USD/t 50 3 Sunflower oil cake USD/t 58 4 Soybean oil USD/t 1265 3 Oleic acid USD/t 1390 3 Glucose USD/t 80 3 Mango kernel USD/t 60 5 WS USD/t 84 3 RH USD/t 100 3 CF USD/t 250 3 Consumables Ethyl acetate USD/t 1050 6 Methanol USD/t 90 6 Inoculum USD/t 40 7 Euro conversion to dollar 1.0 € = 1.09 USD (Banco de España, accessed on 4 May 2020); 1 https://tarifaluzhora.es/ info/precio-kwh (accessed on 4 May 2020); 2 Cost given by default in the modeling software; 3 Price provided by the producing companies; 4 Estimated cost for a process similar to WOC production; 5 Estimated cost for imported mango kernel to Europe; 6 https://www.echemi.com/ (accessed on 4 May 2020); 7 Internal estimations at Fraunhofer Chile Research. Cost estimation for this project is a Class 5 under the Cost Estimate Classification System provided by AACE International to evaluate, approve, and/or fund projects. Class 5 estimates a concept screening using methods, such as capacity factoring, parametric models, judgment, and analogies to estimate values in projects with a low definition (0–2%), for which ample accuracy values for low (−20–50%) and high (+30–+100%) ranges can be expected. The unit production cost (UPC) was defined as the price derived in a zero net present value project using the method of capital and operating cost discounting, and it was calculated based on 1 kg of SL crystal produced. Key technical choices were evaluated through their impact on the UPC of the SL crystals as the final product. 2.5.2. Revenue Revenue will be generated from the sales of products with an estimated price for commercial biobased sophorolipids of 10 USD/kg, as obtained by several online providers. 2.5.3. Profitability Analysis The profitability was analyzed by evaluating net production cost, UPC, net present value (NPV), internal rate of return (IRR), and payback time as stated elsewhere [34]. Briefly, the net present value shows how profitable a project will be versus alternatives, the internal rate of return is the expected return on a project, while the payback period determines how long it would take a company to recover the original investment. By using these three parameters, a project can be evaluated on its feasibility. The analysis will compare the base scenario, that accounts for the SL production in a brand-new facility, with a new scenario considering the plant located inside an existing edible oil refinery. In both cases the SSF process configuration (equipment, mass balance, etc.) is the same. This comparison intends to determine whether the construction of the Energies 2022, 15, 4077 10 of 23 plant inside the edible oil refinery has advantages, since requirements in infrastructure and maintenance would be lower and installed facilities and services could be used. 2.6. Equipment Selection Table 3 presents the equipment selected to produce SL extract by SSF using WOC as the substrate and WS as the base scenario. Free on board (FOB) prices were corrected in the simulation by using a 1.25 factor. Equipment was overestimated at a maximum of 75% of usage for further plant expansion. Table 3. Main equipment to produce SL crystals by using SSF, as modelled in Figure 1. Purchase Cost Capacity (Design Equipment Name Comments (USD) Parameter) Tank V-101 Sterilization 11,100 6 m3 Reactor tank V-102 Fermentation 10,513 5 m3 Extraction Reactor tank V-103 Includes air 3200 5 m3 filtration Compressor G-101 – 10,900 10 L/h P and F filter PFF-101 – 41,000 350 kg/h Screw press SC-101 – 20,000 150 kg/h Evaporator EV-101 – 5500 550 kg/h Drum dryer DDR-102 – 5000 150 kg 2.7. Sensitivity Analysis A sensitivity analysis was carried out using the @RISK software (Palisade), addressing uncertainty using the Monte Carlo risk assessment (MCRA). The following critical variables were addressed using a Beta Normal distribution. Summaries of the resulting distribution of effect estimates are then presented as within the 95% confidence limits. Moreover, a scale-up of the process for treating up to 3750 t of WOC (5× times the base scenario) was also assessed. Critical variables assessed, and their ranges, are indicated in Table 4. Table 4. Variables used for the sensitivity analysis. Variable Range Evaluated Value Used in the Base Model Fat conversion into SL (%) 50–80 72 Solvent loss (%) 4.5–5.5 5.0 Scaling up factor (%) 76.5–93.5 85.0 Solvent consumption (v/w) 4.5–5.5 5.0 Residue treatment cost 49.1–60.0 54.5 (USD/t) Ethyl acetate price (USD/t) 855–1.045 950 Hydrophobic substrate cost 0–200 58 (USD/t) Hydrophilic substrate cost 0–250 167 (USD/t) Support cost (USD/t) 0–200 84 3. Results and Discussion 3.1. Mass Balance Mass balances for the base scenario using WS as the support material are presented in Figure 2 including sterilization, fermentation, SL extraction, and recovery stages, and refer to the flows presented in Figure 1. For the base scenario, during the sterilization process, 2.8 t of material (a mixture of WOC, MOL, and WS) were processed per batch, stored daily, Energies 2022, 15, 4077 11 of 23 and sent to the SSF process (S-106). This balance gives approximately 2173 t/y of sterilized material, of which 506 t/y of WOC-derived fats are bioconverted into SL (S-107). After fermentation, the biomass is fed into the extraction process at rates of 2.6 t/d per module. Annually, Energies 2022, 15, x FOR PEER REVIEW the filtrate stream (S-113) after SL extraction is approximately 17,000 m3 ,11which of 23 is delivered to the SL recovery process. Residual biomass from the extraction process is further delivered to anaerobic digestion accounting for 2569 t/y (S-111). In the recovery process, 384 t of SL (S-115) are obtained from the processing of 750 t WOC on a year-round year-round basis. basis.toAccording According to this, theline, this, the processing processing line, as as proposed proposed here, here, accounted foraccounted near 0.2%for of near 0.2% of the European market the European market for biosurfactants.for biosurfactants. Figure 2. major flows of the mass balance for the production of sophorolipids through Solid-State Figure 2. Major flows of the mass balance for the production of sophorolipids through Solid-State Fermentation. 1 Inert materials, not used in the bioconversion; 2 Material not considered as soluble Fermentation. 1 Inert materials, not used in the bioconversion; 2 Material3 not considered as soluble carbohydrates and fats from MOL or WOC (proteins, fiber, and ash). 3 Water added to the solid carbohydrates and fats from MOL or WOC (proteins, fiber, and ash). Water added to the solid mixture to reach water content suitable for yeast growth; 4 SL includes sophorolipids and impurities; mixture to reactive reach water content suitable for yeast growth; 4 SL includes sophorolipids and impurities; 5 Includes and non-reactive materials apart from fats entering the fermentation stage and 5 Includes reactive and non-reactive materials apart from fats entering the fermentation stage and cell proliferation during fermentation. cell proliferation during fermentation. 3.2. Overall Financial Performance 3.2. Overall Financial Performance Simulation results were used to calculate the capital costs (CAPEX) and operating Simulation results were used to calculate the capital costs (CAPEX) and operating costs costs (OPEX) of the most relevant equipment (sterilization tank, fermentation vessel, ex- (OPEX) of the most relevant equipment (sterilization tank, fermentation vessel, extraction traction equipment, filters, evaporator, and dryer), in addition to the annual operating equipment, filters, evaporator, and dryer), in addition to the annual operating costs (labor, costs (labor, facility maintenance, consumables, etc.). facility maintenance, consumables, etc.). The investment was similar for the three scenarios, with an average value of 1303 +/− The investment was similar for the three scenarios, with an average value of 1303 +/− 68 USD per ton of WOC to be processed. The highest contribution to the investment was 68 USD per ton of WOC to be processed. The highest contribution to the investment was the fermentation the fermentation stage stage(72%) (72%) due due to the to the volume volume restriction restriction (5 m3(5 m3) prevents ) that that prevents using using larger larger tanks, as utilized in SmF. The extraction and sterilization stages tanks, as utilized in SmF. The extraction and sterilization stages accounted for 17 and 10%, accounted for 17 and 10%, respectively, respectively, while investment while investment for solvent for solvent recovery wasrecovery less thanwas less SL 1%. For than 1%. For SL production by production by SSF, CAPEX was 6.7, 7.7, and 7.5 MM USD for SSF, CAPEX was 6.7, 7.7, and 7.5 MM USD for WS, RH, and FC scenarios, respectively.WS, RH, and FC scenarios, respectively. Jiménez-Peñalver et al. [46] pointed out some disadvantages of using SSF, some of which Jiménez-Peñalver can be difficult et foral.scaling [46] pointed up, such out as somefewdisadvantages commercially of using SSF, available some of bioreactors which can be difficult for scaling up, such as few commercially available (increasing the investment costs) and the difficulty of product recovery. This last constraint bioreactors (in- creasing is major forthedownstream investment costs) and the because technologies difficultythere of product recovery. is a conflict Thisenvironmental between last constraint is major and impacts for downstream technologies costs for suitable process because there is a conflict implementation. between It is desirable thatenvironmental a large-scale impacts and costs for suitable process implementation. It is facility does not work with organic solvents. Further research is needed to providedesirable that a large-scale data on facility does not work with organic solvents. Further research is the performance of more environmentally friendly DSP technologies in SSF systems, such needed to provide data as on the performance of more environmentally friendly DSP technologies aqueous two-phase systems or supercritical CO2 extraction. This is also a drawback in terms in SSF systems, such of as aqueous scaling two-phase up the plant, whichsystems should be oraddressed. supercriticalAnCO 2 extraction. option for this isThis is also a draw- the implementation back of in terms large, of scaling conditioned up theas chambers plant, which utilized for should be addressed. cheese ripening. An option of The adaptation forthis thistype is the of implementation chamber as a SSFof large,isconditioned facility currently underchambers as utilized evaluation because forthe cheese ripening.of fermentation The adap- biomass tation in traysofpromotes this typebetter of chamber as a SSF gas transfer, andfacility is currently temperature under evaluation and humidity maintenance because the [47]. This fermentation of biomass in trays promotes better gas transfer, and temperature and hu- midity maintenance [47]. This may also represent an easier manipulation, lower the in- vestment and operational costs, and allow for an easier scaling up. Operational expenditure (OPEX) for SL production from WOC by solid-state fermen- tation was 1.9, 2.4, and 6.8 MM USD/y for WS, RH, and FC scenarios, respectively. Cost Energies 2022, 15, 4077 12 of 23 may also represent an easier manipulation, lower the investment and operational costs, and allow for an easier scaling up. Operational expenditure (OPEX) for SL production from WOC by solid-state fermen- tation was 1.9, 2.4, and 6.8 MM USD/y for WS, RH, and FC scenarios, respectively. Cost distribution was similar for the three scenarios, with facility as the major contributor with approximately 60% of the OPEX (1274 ± 67 M USD/y), followed by utilities (13% of OPEX, 290 ± 10 M USD/y), consumables (9% of OPEX, 196 ± 40 M USD/y) and residue treatment (7% of OPEX, 148 ± 35 M USD/y). Electricity consumption accounted for more than 75% of the total cost for the utility item. This was expected because of aeration and intermittent agitation required during fermentation and the solvent evaporation and condensation pro- cesses during the recovery of the ethyl acetate, both known as energy-intensive processes. The recovery stage accounted for almost 50% of the electricity consumption due to solvent evaporation, while the extraction stage was not relevant in the electricity consumption, mainly because extraction is carried out at room temperature in this model. These results are in agreement with those indicated by Krieger et al. [48] in that downstream processes using solvent extraction will probably have high importance in costs and environmental impact when organic solvents are used. This provides a major critical point to analyze in future work on extraction technologies or conditions, specifically in the solvent/biomass ratio or in its complete replacement. Consumables changed mainly because of the solvent consumption rather than equip- ment replacement parts or other chemicals, with slightly higher values for the RH scenario as discussed above. Labor was similar for the three scenarios because even when equip- ment size was enlarged, especially for the RH scenario, it was considered that the same number of operators would be required to run the plant. Raw materials corresponded to less than 10% of the processing cost ranging from 78 to 210 M USD/y for WS and WHC scenarios, respectively, as they are extremely cheap as by-products from other processes, and producing companies are even paying for biomass transportation for its disposal. This result agrees with the 10 to 30% indicated by Mukherjee et al. [49] for raw material costs in a biotechnological process. However, it must be considered in a future scenario when WOC suppliers outside the original facility will charge for the raw material. Maintaining a low raw material cost is critical for the viability of the system. Comparing SSF to SmF, Ashby et al. [33] reported that the cost of the raw materials account for almost 90% of the total costs when glucose and oleic acid are used as the carbon source. From this, we could infer that the processing cost is considerably lower for SmF, probably due to (i) the more scalable equipment for fermentation and (ii) the recovery of the SL. These compounds are separated directly from the broth by centrifugation and filtration without needing an extraction step because no solid matrix was involved. Some authors have reported that downstream processes contribute up to 60% of the total production costs of biosurfactants [19,50], lower than the results obtained here (the extraction and recovery stages contributing to 80% of the OPEX). However, most of the research regarding SL has been carried out using SmF, in which higher operational costs can be observed during the fermentation stage, lowering the relative impact of the downstream stages on the cost. In terms of the final product, UPC values were calculated at 5.1, 5.7, and 6.9 USD/kg SL crystals for WS, RH, and CF scenarios, respectively. When we compare the UPC values for the three scenarios, WS is the best support material choice to produce SL using SSF in terms of the economy of the process. Even when the SL yield is slightly higher using rice husk [30], bulk density and acquisition costs increase the UPC: the equipment needs to be larger, solvent consumption increases along with the recovery energy involved, and the residue treatment cost is also greater than those of the other two scenarios. As stated above, UPCs for SL for the three scenarios are higher than the commonly used petroleum-based surfactants, 1 to 4 USD/kg for synthetic surfactants, such as sodium dodecyl sulphate [51]. However, is it necessary to compare the cost for a product obtained in a bioeconomy framework to its petroleum-based analogue? From a systemic point of view, the answer to this question should be “no”. Indeed, besides cost, the answer should Energies 2022, 15, x FOR PEER REVIEW 13 of 23 Energies 2022, 15, 4077 13 of 23 3.3. Financial Comparison for SL Production with Different Support Materials integrate environmental impact, land use, water consumption, labor qualification, etc. [52] For scenario comparison, the flow diagram and process lines for the three support to account fairly for the advantages of the bioeconomy. On the contrary, it is fair to compare materials are equivalent. the proposed process toInthe terms of infrastructure, production of SL through nosubmerged major changes were made when fermentation. supports varied, rather than re-sizing equipment capacity due to the bulk density and WHC3.3. Financial of the Comparison biomass. lignocellulosic for SL Production These with Different variables Support Materials impacted the amount of solids and water in the Forslurry, andcomparison, scenario changed solventthe flow consumption diagram andduringprocessSL extraction lines and support for the three the quan- tity ofmaterials biomass are equivalent. residues to beIn terms of of. disposed infrastructure, no major was The most intensive changes the were made when RH scenario, with supports varied, rather than re-sizing equipment capacity due to a daily consumption of 49 t/d, while the CF and WS scenarios consumed 37 and 33 t/d, the bulk density and WHC of the lignocellulosic respectively. Consequently, biomass. the RH These variables scenario impacted showed the amount a higher impactofinsolids termsandof water residue in the slurry, and changed solvent consumption during SL extraction and the quantity of production, with 10 t/d, while the CF and WS scenarios produced 8 and 6.6 t/d, respec- biomass residues to be disposed of. The most intensive was the RH scenario, with a daily tively.consumption Due to the remaining solvent of 49 t/d, while imbibed the CF and WSin the solid scenarios matrix,37 consumed if and more33solid residues are t/d, respectively. produced, more solvent Consequently, the RH is lost on ashowed scenario daily basis. a higher impact in terms of residue production, In with 10 t/d, while the CF and WS scenariosthree Figure 3, financial parameters for the supports produced 8 and (wheat 6.6 t/d, straw, rice husk respectively. and Due to coconutthefiber) remainingfor a solvent processimbibed in an existing edible in the solid oil plant matrix, if moreand in aresidues solid new facility (base sce- are produced, nario)more solvent is lost are presented. on a daily basis. Specifically, Figure 3 presents the following parameters: unit pro- duction cost In Figure (UPC);3,capital financial parameters costs (CAPEX); foroperational the three supports (wheat straw, costs (OPEX); paybackrice time; husk and inter- coconut fiber) for a process in an existing edible oil plant and in nal rate of return (IRR); and net present value (NPV) as defined in the materials anda new facility (base scenario) meth- are presented. Specifically, Figure 3 presents the following parameters: unit production cost ods section. (UPC); capital costs (CAPEX); operational costs (OPEX); payback time; internal rate of re- turn (IRR); and net present value (NPV) as defined in the materials and methods section. Figure 3. Financial parameters for the three supports (wheat straw, rice husk and coconut fiber) for a process in an existing edible oil plant and in a new facility (base scenario). Figure 3. Financial parameters for the three supports (wheat straw, rice husk and coconut fiber) for a process inUPC an existing values edible are inoil theplant rangeand ofinother a new facility (base reported valuesscenario). for biobased sophorolipids, with prices up to 10 USD/kg being obtained from several commercial providers. Ashby UPC et al.values are in the [33] reported range2.0 between of and other 3.7reported USD/kgvalues SL in afor biobased sophorolipids, glucose/oleic acid-SmF system, with priceswhich up toare10atUSD/kg least 40%being lowerobtained comparedfrom several with the commercial 5.1 USD/kg providers. SL for the Ashby WS scenario et al. selected as the best [33] reported case for 2.0 between SL production by SSF. SL and 3.7 USD/kg However, ref. [33] simulated in a glucose/oleic a larger acid-SmF quantity system, of which product (90,000 t of SL per year), while in this model, only 384 t of SL per are at least 40% lower compared with the 5.1 USD/kg SL for the WS scenario selected as year are produced. In another work, Van Bogaert et al. [32] indicate that the price for SL production by SmF the best case for SL production by SSF. However, ref. [33] simulated a larger quantity of depends on substrates and production scale, ranging from 2.4 to 5.9 USD/kg. Moreover, product (90,000 t of SL per year), while in this model, only 384 t of SL per year are pro- Wang et al. [34], in a waste biorefinery approach, reported a net production cost from 16.45 duced. In another work, Van Bogaert et al. [32] indicate that the price for SL production by SmF depends on substrates and production scale, ranging from 2.4 to 5.9 USD/kg. Moreover, Wang et al. [34], in a waste biorefinery approach, reported a net production cost from 16.45 to 19.65 USD/kg, higher than the UPC values obtained here, while Soares Energies 2022, 15, 4077 14 of 23 to 19.65 USD/kg, higher than the UPC values obtained here, while Soares da Silva et al. [53] indicated a production cost of 20 USD/kg for a system using 2% canola waste frying oil and 3% corn steep liquor in a volume of 50 L. Still, the economy of scale does not apply the same for SSF systems since fermenter volumes are limited due to solids handling and heat transfer limitations. When we assumed that the SL production scheme would be implemented in the existing facility where WOC is produced, some costs were lowered. In terms of income, if we target a selling price of 10 USD/kg SL, annual revenues will be maximized by the WS scenario (1892 MUSD/y), followed by the RH and CF scenarios with revenues of 1768 and 1011 MUSD, respectively. Under the assumptions made in this study, the return of the inversion can be achieved during the second year of the plant operation (1.3, 1.2, and 1.8 years for WS, RH, and CF scenarios, respectively). Because no SSF industrial facility for SL production is currently ongoing, it is necessary to validate these results in further studies. Moreover, to reduce costs, some aspects of our proposal can be further addressed, as mentioned above, by re-defining the fermentation system or the DSP technology. In the base scenario of a newly constructed plant, NPV values of 6401, 4515 and 59 M USD with IRR of 31, 24, and 12% can be expected for the WS, RH, and CF scenarios, respectively. For the WS scenario, payback time was 3.2 years. As an alternative scenario, the plant can be located inside the same edible oil refinery where the WOC is produced, thus lowering infrastructure requirements and maintenance, and using already installed facilities and services. This way, NPV is calculated as 13,178, 12,231 and 7595 USD for WS, RH, and CF. Payback time was calculated close to a year for WS, giving a good margin for the next iterations of the model. In this comparison, the difficulty of expansion when the process is located inside an established company with a different business core should be considered. This way, a newly installed facility for SL production may be the most attractive option even if the costs are almost double. Up to this point, all scenarios have been compared against each other; however, one of the reasons for studying three lignocellulosic supports relates to seasonality. Therefore, even when the CF scenario is not economically feasible on its own, the complementarity of these three supports options should give a suitable scenario for SL production at a larger scale. For this, it is important to evaluate the annual periodicity and availability of these materials for further economic analysis. Nevertheless, a 12% of interest rate was used in this evaluation as the superior margin for rather new technology. However, lower interest rates should be considered for more robust technology. In terms of the product itself, Wang et al. [34] evaluated two potential outputs: SL crys- tals (about 97% active) and SL syrup (about 78% active) using food waste and raw materials as glucose and oleic acid. The authors indicate the feasibility of this kind of industrial plant for treating domiciliary wastes for SL production; however, their SL recovery process is different from the one proposed here. The crude SLs were obtained by sequential clarifying processes with the removal of the impurities by a few washing steps with a final step of ultrafiltration. This way, the solid residues are free of solvents, and they can be sold for feed. Moreover, they indicate a higher selling price for the SL crystals (38,460 USD/t) than that proposed here and point out that the profitability of producing SL is also significantly higher than other biobased products. This gives us a wide range of opportunities to evalu- ate the best way to produce SL by adapting the technologies, the operational conditions, or the product presentation. Nevertheless, the commercialization of sophorolipids includes low- (detergents) and high- (pharmaceutics) value markets. Therefore, the quality of the SL should be taken into account when the selling price is set. 3.4. Financial Comparison for SL Production from Different Substrates In Figure 4, financial parameters obtained for the different substrates reported by [41] (frying oil-MOL, oleic acid-MOL, WOC-glucose, frying oil-glucose, and oleic acid-glucose), and by [25] (mango kernel-glucose), are presented. Results indicate that UPC values are in the range of 3.7 to 4.3 USD/kg SL, up to 16% lower than those obtained in our model vant result, highlighting that in SSF systems the physical properties (bulk density, WHC) define process costs beyond substrate purchase costs and process yields. Besides, these results indicate that it is feasible to work with other residues in terms of investment and process conditions for SSF. Energies 2022, 15, 4077 When the results using the information indicated in [25] are compared, the UPC of 15 of 23 SL production using mango-kernel fat (MKF) was considerably higher compared with the WOC and MOL scenarios (8.7 and 5.1 USD/kg SL, respectively), mainly because of the equipment with WOC and needed MOL.to process the mango These authors kernelhigher indicated to obtain thefor yields fatall andtheitsconditions physical charac- tested teristics. In this scenario, financial performance is very poor if the compared with those obtained in this work, in which SL production yields using minimum selling price frying oil (MSP) and is set at glucose as 10 theUSD/kg SL. were substrates For this MKF30% nearly case, MSP should higher, be 11.5 while using andacid oleic 14.3and USD/kg for glucose a NPV gave equal twice almost to 0 orthe foryield having a similar obtained byNPV as the model fermenting WOCproposed and MOL. here, Even respectively. when the Even whenpresented conditions this MSP isbynot theconsiderably authors werehigher (43% compared not optimized to the proposed for industrial value), it or semi-industrial will affect they processes, the process gave us feasibility, a preliminaryas mango kernel vision of is not a impact the financial common substrate substrates of changing in Europe and its from obtention, residues maintenance, (WOC, frying oil, and MOL) processing to rawwill be more(oleic materials expensive acid andthanglucose). WOC orUsingother oily residues pure substrateseasily obtained implies higherin yields this geographic area. substrates costs that affect UPC in and also higher opposite ways. Figure 4. Financial parameters for the SLs production scenarios using different substrates. Nomen- clature: WOC: Winterization oil cake; MOL: sugar molasse. (1) Jiménez-Peñalver et al., [41] (2); Parekh et al. [25]. In the SSF process modelled here, the best financial performance could be obtained by using oleic acid and glucose as substrates, with an UPC value of 4.1 USD/kg, but with an IRR of 70% and payback time of 1.5 y. Nevertheless, when frying oil and MOL were used as substrates, UPC was 3.95 USD/kg SL and payback time was 2.1 y, with an IRR of 49%, an up to 20% better performance than using WOC and MOL as proposed in this work. These differences were mainly because of the physical characteristics of the different substrates, which modified the processing volumes, thereby lowering costs associated with the equipment and the overall plant (infrastructure and maintenance). This is a relevant result, highlighting that in SSF systems the physical properties (bulk density, WHC) define process costs beyond substrate purchase costs and process yields. Besides, these results indicate that it is feasible to work with other residues in terms of investment and process conditions for SSF. When the results using the information indicated in [25] are compared, the UPC of SL production using mango-kernel fat (MKF) was considerably higher compared with the WOC and MOL scenarios (8.7 and 5.1 USD/kg SL, respectively), mainly because of the equipment needed to process the mango kernel to obtain the fat and its physical characteristics. In this scenario, financial performance is very poor if the minimum selling price (MSP) is set at 10 USD/kg SL. For this MKF case, MSP should be 11.5 and 14.3 USD/kg for a NPV equal to 0 or for having a similar NPV as the model proposed here, respectively. Even when this MSP is not considerably higher (43% compared to the proposed value), it Figure 4. Financial parameters for the SLs production scenarios using different substrates. Nom clature: WOC: Winterization oil cake; MOL: sugar molasse. (1) Jiménez-Peñalver et al., [41] (2) Energies 2022, 15, 4077 rekh et al. [25]. 16 of 23 3.5. Financial Comparison for SL Production from Different Fermentation Technologies will affect the process feasibility, as mango kernel is not a common substrate in Europe and In Figure 5, financial results using soybean oil (SO) and sunflower oil cake (SOC its obtention, maintenance, and processing will be more expensive than WOC or other oily SmF and residues SSFobtained easily are presented. In this case, in this geographic area.SL production using SOC and SO as substr also produced better results than using WOC and MOL. The UPC value was 4.2 k 3.5. (18% Financial lowerComparison for SL scenario), than the base Production from butDifferent similarFermentation Technologies to that obtained by using experime data from [41], also giving better financial performance. Again,oilthis In Figure 5, financial results using soybean oil (SO) and sunflower cake (SOC) bymeans probably SmF and SSF are presented. In this case, SL production using SOC and SO as substrates across all scenarios studied for substrates, co-substrates and conditions in SSF, yield also produced better results than using WOC and MOL. The UPC value was 4.2 kg/SL provements are not as important as the physical properties and material cost. As (18% lower than the base scenario), but similar to that obtained by using experimental data cussed from above [41], for substrates also giving and support better financial materials, performance. density Again, this directly probably meansdetermines that, across equipm volume all scenariosand operation studied time. This for substrates, affects investment co-substrates and conditions costs and in SSF, operating yield costs for ma improvements are not as important as the physical properties and material cost. ulation and downstream processing. In addition, the material cost effect is due As discussed above for to the a substrates and support materials, density directly determines equipment mal difference between a pure substrate (1265 and 1390 USD/t for soybean oil and o volume and operation time. This affects investment costs and operating costs for manipulation and acid, respectively) compared with residues (60 USD/t for WOC and SOC), up to 23 ti downstream processing. In addition, the material cost effect is due to the abysmal difference more expensive. between For instance, a pure substrate if we (1265 and 1390 consider USD/t the best for soybean yield oil and obtained oleic by [41] using o acid, respectively) acid and glucose, but maintaining the WOC and MOL acquisition compared with residues (60 USD/t for WOC and SOC), up to 23 times more expensive. For costs, the UPC creases 45% instance, if weto 2.8 USD/t consider SL. There the best is a potentially yield obtained largeoleic by [41] using field forand acid investigating glucose, but operati maintaining the WOC and MOL acquisition costs, the UPC decreases conditions on SSF economics; also, better technology needs to be developed 45% to 2.8 USD/t SL. due to There is a potentially large field for investigating operational conditions operational restrictions of the current SSF fermenters, which add costs that industrialon SSF economics; also, better technology needs to be developed due to the operational restrictions of the do not present (i.e., maximum reactor volume, flow air restrictions, multiple sterilizat current SSF fermenters, which add costs that industrial SmF do not present (i.e., maximum of the solid reactor volume,substrate, flow air and intensive restrictions, labor).sterilizations of the solid substrate, and multiple intensive labor). Figure 5. Financial parameters for the SLs production scenarios using sunflower oil cake (SOC) and soybean oil (SO) as substrates. SSF: Solid State Fermentation. SmF: Submerged fermentation. SmF Figure 5. produced Financial aparameters higher UPCfor than thethat SLsobtained for SSF production under the scenarios conditions using proposed sunflower oil cake (SOC) by [12] and similar to our base case. However, financial indicators were better for SmF soybean oil (SO) as substrates. SSF: Solid State Fermentation. SmF: Submerged fermentation. considering the total investment, NPV and payback time. When compared with the results from [33], it is noteworthy that even when UPCs for the latter were almost half of those SmFinproduced obtained a higher this simulation, UPC volumes processing than thatareobtained for SSF lower still considerably underthan thethose conditions posed by [12] and similar to our base case. However, financial indicators previously reported by [33] (2.95 USD/kg SL for a production capacity of 17.1 t SL/y were bette SmF considering the total investment, NPV and payback time. When compared with results from [33], it is noteworthy that even when UPCs for the latter were almost ha those obtained in this simulation, processing volumes are still considerably lower Energies 2022, 15, x FOR PEER REVIEW 17 of 23 Energies 2022, 15, 4077 17 of 23 those previously reported by [33] (2.95 USD/kg SL for a production capacity of 17.1 t SL/y compared with 5.2 USD/kg SL for a production of 384 t SL/y). Under this consideration, SmF values obtained in this simulation are according to those expected for a techno-eco- compared with 5.2 USD/kg SL for a production of 384 t SL/y). Under this consideration, nomicvalues SmF analysis of a small obtained processing in this simulationplant, areconsidering according to that [12]expected those reported forthataSSF had techno- better yields (in g SL/g substrate) by using a methanol/ethyl acetate double economic analysis of a small processing plant, considering that [12] reported that SSF had extraction compared better with yields (in an SmF with g SL/g a single substrate) by methanol extraction. The acetate using a methanol/ethyl combination doubleofextraction these fac- compared with an SmF with a single methanol extraction. The combination of these should tors affected the UPC for SmF. Therefore, when comparing SmF to SSF, not only factors the yieldthe affected be taken UPC forintoSmF. account, but also Therefore, the processing when comparingvolume SmF toandSSF,the operational not only should differ- the ences. In this sense, increasing the facility scale would be more advantageous yield be taken into account, but also the processing volume and the operational differences.for the SmF scenario In since this sense, current commercial increasing technologies the facility scale would befor SmF more allow for higher advantageous for thefermenter vol- SmF scenario umes than for SSF. Thus, the limitation by the maximum fermenter volume since current commercial technologies for SmF allow for higher fermenter volumes than would be less restrictive, affecting UPC and other financial parameters. for SSF. Thus, the limitation by the maximum fermenter volume would be less restrictive, affecting UPC and other financial parameters. 3.6. General Scenarios Comparison 3.6. General Scenarios In Figure Comparison 6, a comparison of the investment for SL production by different substrates, supports, In Figureand 6,technology a comparison is presented. In this work, of the investment for SLseveral productionfactors were considered by different substrates,in supports, terms of costs,and equipment, technology yields, is presented. In this work, and materials, amongseveral others,factors which were obtained considered in from terms our own of costs, equipment, experiments yields, and from and authors. other materials, among others, However, as the which were obtained SL production from proposal in our this own studyexperiments is still underand from other evaluation andauthors. more pilot However, studies areas the SL production currently proposal being carried out, in wethis study is an developed stillindicator under evaluation to give the andestimation more pilotofstudies are currently the equipment being carried acquisition costs, out, we developed an indicator to give the estimation of the equipment which is the most relevant aspect for the feasibility of implementing this SSF technology. acquisition costs, which is the most relevant aspect for the feasibility of implementing When all the evaluated cases including different supports and substrates are compared, this SSF technology. When all the evaluated an investment betweencases including 0.6 and 3.1 USD different supports per kilogram of and substratesshould SL produced are compared, be consid-an investment ered. It is worthbetween 0.6 and noticing that3.1changing USD per the kilogram of SL supports produced impacts less should beUSD/kg (1.9 ± 0.5 considered. SL) It is worth than changing noticing that changing the substrates (2.6 ±the 0.4supports USD/kg SL),impacts mainly (1.9 ± 0.5 lessbecause USD/kg of the SL)prop- physical than changing the substrates (2.6 ± 0.4 USD/kg SL), mainly because of the erties (especially oil density) of the substrates and co-substrates that impact the average physical properties (especially size of the oil density) of and fermentation the substrates extractionand co-substrates equipment. Thesethat impact values the average should size of be considered the when the scaling up of the process is being assessed for having an early estimation of the fermentation and extraction equipment. These values should be considered when the scaling investmentup ofcosts, the process at leastisonbeing assessed the major for having equipment an early needed. estimation It was expectedof the thatinvestment lower val- costs, ues foratSmFleastthan on the formajor equipment SSF would needed.however, be obtained; It was expected under the that lower values conditions usedfor to SmF sim- than for SSF would be obtained; however, under the conditions used ulate SmF and SSF by using data from [12], this was not observed at this time, with values to simulate SmF and SSF by using data from [12], this was not observed at this time, of 0.6 and 0.9 USD/kg SL for SSF and SmF, respectively, and considerably lower (−83%) with values of 0.6 and 0.9 than USD/kg SL for SSF those obtained forand SmF,asrespectively, the SSF proposed here and(2.3 considerably USD/kg SL). lower (−83%) than those obtained for the SSF as proposed here (2.3 USD/kg SL). Figure 6. Investment of the studied cases for SL production by different substrates, supports and technology. (1) Jiménez-Peñalver et al. [41]; (2) Parekh et al. [25]; (3) Rashad et al., [12]. Energies 2022, 15, x FOR PEER REVIEW 18 of 23 Figure 6. Investment of the studied cases for SL production by different substrates, supports and Energies 2022, 15, 4077 18 of 23 technology. (1) Jiménez-Peñalver et al. [41]; (2) Parekh et al. [25]; (3) Rashad et al., [12]. 3.7. Sensitivity Analysis 3.7. Sensitivity SensitivityAnalysis analysis is commonly used in financial modeling to determine how criti- cal, independent variables Sensitivity analysis affect a dependent is commonly variablemodeling used in financial under certain specific how to determine conditions. critical, independent In variables practical terms, affect analysis sensitivity a dependent usingvariable underindicates Monte Carlo certain specific in whichconditions. range of val-In practical ues terms, the UPC willsensitivity vary 90% analysis using of the time Monte if all Carlo indicates the variables change in which range randomly of val- in multiple ues the UPC potential will vary 90% of the time if all the variables change randomly in multiple scenarios. potential scenarios. Figures 7 and 8 present the results for the sensitivity analysis on the net present value (NPV) Figures 7 and and unit 8 presentcost production the results (UPC), for the sensitivity respectively. Underanalysis on the net present the assumptions made invalue this (NPV) and unit production cost (UPC), respectively. Under the assumptions study (Table 4), NPV could range between −2.1 and 10.9 MM USD, with an average made in this value study of 4 MM(Table USD. 4),The NPVcombined could range between effect −2.1 and of multiple 10.9 MM variables in aUSD, with an worst-case averagewould scenario value of 4 MM USD. eventually leadThe to acombined negative NPVeffectvalue. of multiple variables However, in avalues positive worst-case in thescenario range ofwould 1 to 8 eventually MM USD arelead to a negative expected with 90% NPVofvalue. However, probability. UPC positive values values in the may vary range from 3.97ofto1 7.91 to 8 MM USD are expected with 90% of probability. USD/kg SL, with an average value of 5.51 USD/kg SL. UPC values may vary from 3.97 to 7.91 USD/kg SL, with an average value of 5.51 USD/kg SL. Figure 7. Sensitivity analysis analysis ononSL SLproduction productionfrom fromWOC WOCbyby SSF onon SSF thethe netnet present value present (NPV) value for (NPV) for the the WS WS scenario scenario underunder the conditions the conditions assumed assumed in theinmodel. the model. Nomenclature: Nomenclature: WS: wheat WS: wheat straw;straw; WOC: WOC: Winterization Winterization oil cake. oil cake. The analysis showed that the main contributors to the variation in NPV and UPC were the SL yield and the scaling-up factor. If SL yield varies 10% (base case 0.8 kg SL/kg fat), UPC costs will vary near 8%, from 4.7 to 5.5 USD/kg SL. These results highlight that SL yield is a crucial parameter and must be taken into account when considering alternative substrates. Of course, as stated above, the alternative substrates should have a similar acquisition cost, otherwise the positive effect of enhancing the yield will not be relevant to the financial performance of the process. Also, the physical properties could impact overall finances. Equivalent results were obtained for the scaling factor (here defined as the yield reduction due to the change in the operational conditions when upscaling from a pilot-scale to an industrial-scale—base case 15% of yield loss when upscaling). For instance, if the process has no losses during up-scaling, the SL production increases up to 452 t/y (18% more than the base scenario) with a UPC value of 4.3 USD/kg (16% lower). Sensitivity analysis indicates that the fermentation performance has more influence on the UPC costs than the overall performance of the production process in terms of solvent consumption, material costs, etc. These operational conditions affected less than 1.5% when they varied Energies 2022, 15, 4077 19 of 23 Energies 2022, 15, x FOR PEER REVIEW 19 of 23 10%. Therefore, efforts should be made in terms of increased yields, process efficiency, and overall performance in the upscaling stage. unit production Figure 8. Sensitivity analysis on SL production from WOC by SSF on the unit production cost (UPC) scenario under the conditions for the WS scenario conditions assumed in the model. Nomenclature: WS: wheat straw; WOC: Winterization WOC: Winterization oil oil cake. cake. analysis showed The sensitivity analysis that thehighlights also main contributors to the the different variation effects of theinselected NPV and UPC process were the SL variables on yield and theparameters. the financial scaling-up factor. As statedIf SL yieldSL above, varies yield10%and(base case scaling up0.8 kg SL/kg factor, both related fat), UPC to costs SL production will vary nearcapacity, affect4.7 8%, from UPC and to 5.5 NPV. However, USD/kg SL. These solvent consumption results highlight that and recovery SL yield solventparameter is a crucial costs are the andmain mustvariables be takencontributing into accounttowhen CAPEX, while thealterna- considering cost of the tivesupport substrates. material, hydrophobic Of course, as statedand hydrophilic above, substrates the alternative are theshould substrates main contributors have a similar to OPEX variability. acquisition cost, otherwise the positive effect of enhancing the yield will not be relevant to the It financial is worth noting that theof performance impact of the support the process. material, Also, the given physical its characteristics, properties could impact will vary overallthefinances. UPC costs due to the Equivalent quantity results were needed for the obtained finalscaling for the mixture (mainly factor (herebased on its defined as WHC the yieldandreduction bulk density), due to itsthe impact changein the fermentation in the operational (affecting conditions SL yield), and the cost when upscaling fromof its acquisition (including transport and manipulation). a pilot-scale to an industrial-scale—base case 15% of yield loss when upscaling). For in- stance,Due to the if the opportunity process to connect has no losses duringthree edible oilthe up-scaling, refineries operating SL production in the up increases sameto region and thus triple the initial amount of WOC, the effect of 452 t/y (18% more than the base scenario) with a UPC value of 4.3 USD/kg (16% lower). plant capacity on UPC cost was investigated. Sensitivity analysis However, indicatesand thattothe thefermentation authors’ knowledge, performance no packed-bed SSF bioreactors has more influence on the are UPC operating costs than at the a commercial scale with of overall performance thethedesign capacityprocess production used herein. in termsForofthis reason, solvent con-a conservative value for the maximum volume of solid-state fermenters was assumed in 5 m 3. sumption, material costs, etc. These operational conditions affected less than 1.5% when Too theylarge varied fermenters would, efforts 10%. Therefore, undoubtedly, should present be madea difficult in terms temperature of increased control. Due to yields, process these restrictions in the construction and efficiency, and overall performance in the upscaling stage. operation of the SSF reactors, scaling up of the process up to 5 × times was considered appropriate in case more industries The sensitivity analysis also highlights the different effects of the selected process are involved. In this new scenario, no significant changes in the UPC of SL were observed, variables on the financial parameters. As stated above, SL yield and scaling up factor, both varying from 5.1 to 4.3 USD/kg related to SL production (−16% inaffect SL crystalscapacity, the UPCUPC value and for NPV.the However, base case).solvent Based on this model, consumption even with a larger upscaling (up to 5000 t WOC/y), UPC values will not be reduced by and recovery solvent costs are the main variables contributing to CAPEX, while the cost more than 20% due to the main restriction, as previously described, of the SSF equipment. of the support material, hydrophobic and hydrophilic substrates are the main contributors It is expected that if a new configuration of SSF is based on a specially designed chamber, to OPEX variability. the costs in infrastructure and operation would be lowered and the process will become It is worth noting that the impact of the support material, given its characteristics, more economically attractive. will vary the UPC costs due to the quantity needed for the final mixture (mainly based on its WHC and bulk density), its impact in the fermentation (affecting SL yield), and the cost of its acquisition (including transport and manipulation). Due to the opportunity to connect three edible oil refineries operating in the same region and thus triple the initial amount of WOC, the effect of plant capacity on UPC cost was investigated. However, and to the authors’ knowledge, no packed-bed SSF Energies 2022, 15, 4077 20 of 23 4. Conclusions The model proposed here is the first of its kind for SL production via solid-state fermentation (SSF). This model is designed on a circular bioeconomy approach by using by-products from the food industry (Winterization Oil Cake and molasses) as substrates, in which economic results validate its use as an option for SL production via SSF. The obtained unit production cost for SL is in the upper range for the commonly used petroleum-based surfactants and in the range of SL produced through submerged fermentation. Comparison among different substrates indicated that residues can feasibly be used under a techno- economic point of view, even with a slightly better performance than using raw substrates. Thus, demonstrating that SSF can be a feasible technology to valorize solid oily residues from the food industry. Results highlight the relevance of the physical properties of feedstocks and supports in the financial performance of the process. Extraction of the bioproduct from the solid matrix contributes significantly to production costs and thus alternative DSP technologies should be explored. As SSF is still at bench and pilot scale development to produce marketable products from waste, it is important to perform techno- economic and environmental analyses parallel to process development to ensure economic and environmental feasibility of the final SSF process. Author Contributions: Methodology and Writing—original draft: M.M.; Investigation: A.R.; Concep- tualization, Writing—review and editing, Supervision: X.F.; Conceptualization, Writing—review and editing, Supervision, Funding acquisition: T.G. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Spanish Ministerio de Ciencia e Innovación (Project PID2020- 114087RB-I00), the InnovaChile CORFO (Code FCR-CSB 09CEII-699) and the National Council for Science and Technology (CONACYT) México for the scholarship assigned for A. Rodríguez PhD studies. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Nomenclature BS Biosurfactants CAGR Compound Annual Growth Rate CAPEX Capital Expenses (USD) CBA Cost-Benefit Analysis CEPCI Chemical Engineering Plant Cost Index (dimension-less) CF Coconut fiber IRR Internal Return Rate FOB Free onboard MKF Mango kernel fat MOL Molasses MSP Minimum selling price NPV Net present value OPEX Operational Expenses (USD/y) RH Rice husk SL Sophorolipid SmF Submerged Fermentation SOC Sunflower oil cake SO Soybean oil SSF Solid-State Fermentation UPC Unit Production Cost (USD/kg) WHC Water Holding Capacity WOC Winterization Oil Cake WS Wheat straw Energies 2022, 15, 4077 21 of 23 References 1. Carus, M.; Dammer, L. The “Circular Bioeconomy”—Concepts, Opportunities and Limitations. Ind. Biotechnol. 2018, 14, 83–91. [CrossRef] 2. Lewis, A.M. Chronic and sublethal toxicities of surfactants to aquatic animals: A review and risk assessment. Water Res. 1991, 25, 101–113. [CrossRef] 3. Ying, G.-G. 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