Highly Efficient Deacidification Process for Camelina Sativa Crude

sustainability

Article Highly Efﬁcient Deacidiﬁcation Process for Camelina sativa Crude Oil by Molecular Distillation

1 1, 1 2 3 Nicoleta Gabriela ¸Stefan , Petrica Iancu * , Valentin Ples, u , Ioan Călinescu and Nicoleta Daniela Ignat

1 Department of Chemical and Biochemical Engineering, University POLITEHNICA of Bucharest, 1 Polizu Street, 011061 Bucharest, Romania; [email protected] (N.G.¸S.);[email protected] (V.P.) 2 Department of Bioresources and Polymer Science, University POLITEHNICA of Bucharest, 1 Polizu Street, 011061 Bucharest, Romania; [email protected] 3 Department of Economical Engineering, University POLITEHNICA of Bucharest, 313 Splaiul Independentei Street, 060042 Bucharest, Romania; [email protected] * Correspondence: [email protected]

Abstract: Recovery and reuse of high-acidity vegetable oil waste (higher content of free fatty acids) is a major concern for reducing their effect on the environment. Moreover, the conventional deacidification processes are known to show drawbacks, such as oil losses or higher costs of wastewater treatment, for which it requires great attention, especially at the industrial scale. This work presents the design of a highly efficient and sustainable process for Camelina sativa oil deacidification by using an ecofriendly method, namely molecular distillation. Experimental studies were performed to identify operating conditions for removing of free fatty acids (FFA) by molecular distillation which involves the oil evaporation in high vacuum conditions. The experimental studies were supported by statistical analysis and technical-economic analysis. Response surface methodology (RSM) was employed to formulate and validate second-order models to predict deacidification efficiency, FFA concentration, and triacylglyceride (TAG) concentration in deodorized oil based on three parameters Citation: ¸Stefan,N.G.; Iancu, P.; effects, validated by statistical p-value < 0.05. For a desirability function value of 0.9826, the optimal Plesu, V.; C˘alinescu,I.; Ignat, N.D. , ◦ Highly Efficient Deacidification parameters of evaporator temperature at 173.5 C, wiper speed at 350 rpm, and feed flowrate at Process for Camelina sativa Crude Oil 2 mL/min were selected. The results for process design at optimal conditions (using conventional by Molecular Distillation. and molecular distillation methods) showed an efficiency over 92%, a significant reduction in FFA (up Sustainability 2021, 13, 2818. https:// to 1%), and an increase in TAG (up to 93%) in refined oil for both methods. From an economical point doi.org/10.3390/su13052818 of view, the deacidification by molecular distillation of Camelina sativa oil is a sustainable process: no wastewater generation, no solvents and water consumption, and lower production costs, obtaining a Academic Editor: Dino Musmarra valuable by-product (FFA).

Received: 18 January 2021 Keywords: deacidiﬁcation efﬁciency; FFA removing; Camelina sativa oil; molecular distillation; Accepted: 2 March 2021 Box-Behnken design; economic analysis Published: 5 March 2021

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in 1. Introduction published maps and institutional affil- iations. Mono-, di- and triglycerides are the main components of crude oils, in addition to tocopherols, squalene, and sterols in traces (200–800 ppm), whose presence improve oil oxidative stability. Besides these desired components, crude oils contain undesirable impurities as phospholipids (100–500 ppm), free fatty acids (FFA) (5–20%), metal ions and metal complexes (2–15 mg·kg−1), oxidized products (2–6 meq·kg−1), and moisture Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. (1–3%) [1]. FFA content is one of the most important aspects that influences edible oil quality This article is an open access article or their usage as raw material in biodiesel production because they are more susceptible distributed under the terms and to autoxidation than the esterified fatty acids (FFA act like pro-oxidants, initiating the conditions of the Creative Commons oxidation mechanism in lipids, leading to rancidity) or produce soaps (by mixing with Attribution (CC BY) license (https:// methanol for transesterification reaction) [1]. Long storage time and temperature over ◦ creativecommons.org/licenses/by/ 24 C, contribute to the degradation and quick rancidity of oils [2]. Moreover, some 4.0/). fresh crude oil extracted by cold pressing can have high acidity and high FFA content.

Sustainability 2021, 13, 2818. https://doi.org/10.3390/su13052818 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 2818 2 of 18

Therefore, FFA removal is an important step in the crude oil refining process, called oil deacidification. To decrease FFA level of the crude oils, chemical methods (alkali neutralization, re-esterification) and physical methods (absorption, liquid–liquid extraction, stripping at high temperatures and pressures, molecular distillation, supercritical fluid extraction) are used. At industrial scale, chemical deacidification by adding alkali is commonly used. Although the main advantage of the chemical method is the high efficiency in FFA removal below 1%, this method has two major disadvantages in terms of oil losses (15–37%), due to the formation of soaps by retention of oil droplets, and environment pollution, due to the wastewater generated from the removal of soaps by washing and the higher cost of their disposal [3,4]. Another chemical method that is often coupled with the conventional method is FFA esterification with an alcohol in the presence of catalysts at higher temperatures (200–280 ◦C). Recent studies have been reporting higher deacidification efficiencies by using ethanol or glycerol excess in the presence of different catalyst types: heterogenous catalyst as zinc oxide [5], acid ion exchange resin or enzymes [6], and superacid catalyst [7]. However, this method does not have an industrial application due to the high cost of catalysts and the high temperature conditions at which the esterification reaction takes place. On the other hand, oil refining at an industrial level can be achieved by physical methods (mostly steam stripping), which have the advantage of using one piece of equipment and cheaper utilities but are limited for oils that contain thermolabile compounds. Solvent extraction is other feasible deacidification method to decrease FFA from oils and has the advantage of small oil losses, low energy consumption, gentle oil treatment, and reduced waste products. Methanol, ethanol, amyl alcohol, acetone, or acetic ester can be used as solvents [8,9]. The main disadvantage of this process is the oil-solvent systems L–L equilib- rium data required. Additionally, this process requires at least three pieces of equipment: a liquid–liquid extraction column, a stripper for solvent removal from the refined oil, and a column to recover the solvent, so the capital costs and energy consumptions must be taken into consideration. Due to these major drawbacks, alternatives methods need to be used on an industrial scale; to improve oil quality, the solvent traces in purified oils can be replaced using environ- mentally friendly solvents, such as CO2, under supercritical conditions [10]. Degradation of thermolabile compounds from oils can be avoided by using methods such as molecular distillation or advanced vacuum distillation [11–16]. Some researchers showed their interest in improving deacidification process using molecular distillation. They reported the results obtained for oils with different compositions and acidities: grape seed oil—3 mg KOH/g [11], cacao butter—3.16 mg KOH/g [12], palm oil—21.3 mg NaOH/h [13], soybean isolated by aqueous enzyme-assisted extraction—0.57 mg KOH/g [14], hazelnut oil—17.26 mg KOH/g [14], or microalgae—1.2 mg KOH/g [15]. Molecular distillation has been indicated as a reliable method that can be used at the industrial level thanks to advantages such as: rapid evaporation of liquid that is uniformly distributed as a thin film on a hot surface, lower evaporation temperatures by providing a high vacuum, the high deacidification efficiency, and the high purity of products. In comparison with the other deacidification methods, the cost of equipment, vacuum, and heating utilities can be the main disadvantage. Most studies for oil deacidification by molecular distillation have been done at laboratory scale, being oriented on finding the optimal operating conditions that depend on the oil composition. Therefore, a detailed economic analysis for a scale-up at the industrial level of this process is required to show a trade-off between advantages and disadvantages. The aim of this paper was to design a sustainable deacidification process at micro-pilot scale by using molecular distillation as the separation method of FFA. Camelina sativa oil, characterized by a high content of polyunsaturated fatty acids, essential for human diet, was used as a raw material due to the valuable products that can be obtained from refined oil: a mixture of FFA, edible oil, and omega fatty acid esters. Operating parameters were highly influenced by oil composition, and their values and influence on process efficiency Sustainability 2021, 13, 2818 3 of 18

and oil quality, were evaluated by lab-scale experimental investigation and optimized by RSM (based on a higher value of desirability function). Thus, the design of an ecofriendly and energy-efﬁcient process at micro-pilot plant scale is presented.

2. Materials and Methods 2.1. Materials Crude Camelina sativa oil was provided by a Romanian local farm and stored in laboratory conditions for six months. For acidity value determination, diethyl ether (99.7% purity), ethanol purity p.a. (99.8%), and potassium hydroxide solution in methanol 0.5 M from Sigma Aldrich (Munich, Germany) were used. For alkali deacidiﬁcation, sodium hydroxide (98% purity from Sigma Aldrich, Munich, Germany) and hexane (99% purity from Sigma Aldrich, Munich, Germany) were used. As a drying agent, anhydrous magnesium sulfate (98% purity) from Sigma Aldrich (Germany) was used, and n-heptane (99% purity) and acetone (99.5% purity) from Sigma Aldrich (Munich, Germany) were used for the Gas Chromatography with Flame Ionization Detection (GC-FID) analysis.

2.2. Experimental Procedures Two experimental procedures were used for deacidiﬁcation of Camelina sativa oil: chemical (reaction of FFA with alkali) and physical (molecular distillation) methods.

2.2.1. Camelina sativa Oil Deacidiﬁcation Using Alkali This method consisted in treating the oil with an aqueous solution of sodium hydroxide which reacted with FFA, forming soap.

+ − NaOH + H2O Na + HO (1) + − − + Na + HO + R − COOH R − COO Na + H2O (2) Experiments were performed using the method proposed elsewhere [17]. One gram of alkali was dissolved in 1 mL distilled water and further added to a sample of 150 g Camelina sativa oil. The sample was stirred 30 min, and then 15 mL hexane was added and was stirred again for another 30 min. Finally, the soap was separated at 3500 rpm for 30 min to evaporate the solvent. To remove the soap traces, three successive washes with distilled water at 95 ◦C were performed, and washing water was removed by centrifugation. Samples were prepared to determine the acidity value and to identify the content of FFA and triacylglyceride (TAG).

2.2.2. Camelina sativa Oil Deacidification by Molecular Distillation Samples of 500 g Camelina sativa oil were subjected to a wiped film molecular distillation plant (KDL5, UIC GmbH, Alzenau, Germany) to separate FFA based on migration of molecules with different molecular volumes at vacuum conditions. The plant was heated by thermal oil. Working procedure is presented in [18]. The operating conditions were varied in the specific ranges imposed by equipment limits: the evaporator temperature (maximum 250 ◦C), the rotational wiping speed (250–350 rpm), feed flow rate (2–6 mL/min), while pressure (0.001–100 mbar) and condenser temperature (less than 30 ◦C). Light and heavy samples were collected in different devices and prepared to determine the acidity value and to identify the content of free fatty acids and triglycerides by chromatographic analysis. Separation process efficiency was calculated with Equation (3): feed FFA content (g) − bottom product FFA content (g) Deacidification efficiency (%) = ·100 (3) feed FFA content (g) Seventeen experiments were performed by varying operating conditions, such as evaporator temperature, the rotational wiping speed, and feed flow rate in equipment range, to evaluate the influence on deacidification efficiency and TAG and FFA compositions. Sustainability 2021, 13, 2818 4 of 18

2.3. Samples Characterization 2.3.1. Acidity Value Camelina sativa oil sample acidity was determined by a method according to the Euro- pean Standard SR EN No. 660 [19]. The sample was diluted in a mixture of ethanol:diethyl ether 1:1 (v/v) in the presence of phenolphthalein as an indicator and neutralized with 0.5M KOH solution in methanol. The acidity (IA) was calculated using Equation (4), where V is volume of potassium hydroxide solution used in the titration in cm3, n is the normality of solution, m is analyzed sample mass in g, and MKOH is molar weight of potassium hydroxide in g/mol. M ·V·n I = KOH (4) A m

2.3.2. FFA and Glycerides Content Monoacylglycerides (MAG), diacylglycerides (DAG), triacylglycerides (TAG), and FFA compositions were determined by GC-FID using a gas-chromatograph Bruker Scion 436 according to SR EN Standard No. 14105 [20]. For sample analyses, a fused capillary column ZB-5HT type with a nitrogen flow of 5 mL/min was used. Because high temperature is necessary to vaporize the glycerides the injector temperature started at 50 ◦C and increased with the rate of 50 ◦C·min−1 until 380 ◦C with a 2 min hold. Electronic flow controller (EFC) was settled at 2.5 mL/min and the following oven temperature program was used: 50 ◦C for 1 min, rate 15 ◦C·min−1 until 180 ◦C, rate 7 ◦C·min−1 until 230 ◦C, rate 10 ◦C·min−1 until 370 ◦C. Detector temperature (FID) was set at 380 ◦C and the gas flows at 15 mL/min for the make-up gas (nitrogen), 15 mL/min for hydrogen, and 300 mL/min for air. The injection volume used was 1 µL. All samples were analyzed in five replicates and the results are presented as the average value ± standard deviation (SD).

2.4. Statistical Analysis To find optimal operating conditions for the deacidification process of Camelina sativa oil, a design of experiments (DOE) was used. DOE main steps followed for this analysis were: setting up experiments objectives, factors and levels selection (number of independent variables manipulated to determine their effect on the measured responses), selection of dependent variables (measured during experiments, their value depends on independent variable effect), design experimental matrix, select ranges of independent variables and number of replicates of experimental data, collect experimental data, regression model formulation and factor effect evaluation, and validation applying statistical ANOVA and regression methods. Different kinds of dependent or independent variables can be selected for the oil deacidification process, and different optimal parameters can be identified for particular cases [21–26]. Based on this statistical technique, a second-degree model was formulated to describe the system, including effects or interactions of factors, solved by minimizing/maximizing the response variables. As a DOE technique, the Box–Behnken surface experimental design was used in this study to investigate the effects of three factors (evaporator temperature, wiper rolling speed, and feed flow rate), for some responses of the oil deacidification process (minimum FFA content, maximum TGA content). The independent variable limits depended on equipment limits and raw material composition. The Box–Behnken design consists of three levels (minimum, center, and maximum values, K = 3) selected for each independent variable, with coded labels (X1,X2, and X3) and values as presented in Table1. For the molecular distillation process, three dependent variables were explored: the FFA composition in heavy product (Y1), deacidification efficiency (Y2), and the TAG composition in heavy product (Y3), as presented in Table2. Sustainability 2021, 13, 2818 5 of 18

Table 1. Independent variables for design (coded and encoded).

Independent Variables Type ID Minim Value Center Value Maxim Value (Effect Factors) Evaporator Encoded 160 170 180 X temperature (◦C) Coded 1 −1 0 1 Wiper rolling Encoded 250 300 350 X speed (rpm) Coded 2 −1 0 1 Feed ﬂow Encoded 2 4 6 X rate (mL/min) Coded 3 −1 0 1

Table 2. Dependent variables for design (coded and encoded).

Dependent Variables (Responses) Encoded Coded

FFA composition in heavy product (%) Y1 Deacidiﬁcation efﬁciency (%) Y2 TAG compositions in heavy product (%) Y3

The Box–Behnken design matrix contains combination of two variables as for 22 factorial design, the third variable being maintained at level zero and some central point values. A second-order model (in coded variables) was formulated to provide optimal surfaces for each response Yi using Equation (5).

Yi = β0,i + β1,iX1 + β2,iX2 + β3,iX3 main effect

+β4,iX1X2 + β5,iX2X3 + β6,iX1X3 interaction effect (5) 2 2 2 + β7,iX1 + β8,iX2 + β9,iX3 quadratic effect

Considering three Xi factors, the model has M coefficients βj,i for each i responses (i = 1 ... K, j = 0 ... M) which represent the change of responses Yi per unit of effect of independent variables Xi, and K number of factors. The regression models for responses considered main effects (X1,X2,X3) determined by the influence of each variable independent from the others, interaction effects (X1X2, X1X3,X2X3) described as a cumulative effect of two dependent variables and the quadratic 2 2 2 effect of each variable (X1, X2, X3). The model coefficient values were generated by the least squares regression followed by analysis of variance (ANOVA) for model validation. The total number of experiments (N) for second-order model was calculated using Equation (6):

2 N = K + K + Cp (6)

where K is the factors number and Cp is the replicates at the central point. Thus, for Box–Behnken model design, 17 experimental runs were performed with five replications at the central point. For the Camelina sativa oil deacidification process, the Box–Behnken matrix with three factors in coded form and three responses is shown in Table3. The optimal values of dependent variables were predicted using desirability function. The model lack of fit was calculated in terms of coefficient of determination (R2) and p-values for 95% confidence interval considered for analysis of variance (ANOVA). The optimal values of factors (minimum/maximum) were identified on their response surfaces for acceptable values of fitted responses evaluating desirability functions. Sustainability 2021, 13, 2818 6 of 18

Table 3. Box–Behnken design matrix deacidiﬁcation process by molecular distillation.

Factors Responses Run (Coded Variables) (Encoded Variables)

X1 X2 X3 Y1 Y2 Y3 1 −1 −1 0 3.37 58.37 58.37 2 +1 −1 0 2.46 73.33 73.33 3 −1 −1 0 2.73 80.17 80.17 4 +1 −1 0 2.44 87.52 87.52 5 −1 0 −1 2.13 73.92 73.92 6 +1 0 −1 1.61 80.39 80.39 7 −1 0 +1 3.05 61.00 61.00 8 +1 0 +1 2.61 76.78 76.78 9 0 −1 −1 2.46 69.96 69.96 10 0 −1 +1 0.88 91.75 91.75 11 0 −1 −1 2.77 66.39 66.39 12 0 −1 +1 2.64 80.12 80.12 13 0 0 0 2.51 75.34 75.34 14 0 0 0 2.45 75.32 75.32 15 0 0 0 2.53 75.35 75.35 16 0 0 0 2.44 75.34 75.34 17 0 0 0 2.62 75.33 75.33

2.5. Process Design, Economic and Environmental Analysis Molecular distillation processes were designed using Aspen Plus v.10 (AspenTech Technology Inc., Bedford, MA, USA, 2017) simulator to perform mass and energy balances, equipment design, and total costs. Oil sample was considered as a mixture of mono-, di- and triglycerides; tocopherols; and fatty acids identiﬁed as majority in composition of Camelina sativa oil. All compounds are in the simulator database and their properties were calculated using RK-ASPEN (Redlich–Kwong Soave with Wong–Sandler mixture rules) properties package [27]. The molecular distillation unit was modeled by combination of three simulator models: HeatX–Flash2–HeatX, for light molecule evaporation, vaporized molecule migration, and condensation, with mixture temperature and pressure as speciﬁcations. Economic analysis was performed using Aspen Plus v.10 simulator.

3. Results and Discussions According to the GC-FID analysis, the fresh press extraction of Camelina sativa oil, pur- chased from a regional farm, contained 3.23 ± 0.33% FFA, 0.75 ± 0.10% MAG, 5.95 ± 0.24% DAG, and 90.07 ± 0.32% TAG and has an acidity value of 3.35 ± 0.10 mg KOH/g oil. After six months of storage, chromatographic analysis of the same oil showed a modiﬁed composition: 7.67 ± 0.33% FFA, 0.84 ± 0.09% MAG, 6.01 ± 0.30% DAG, and 85.49 ± 0.42% TAG and acidity value of 7.17 ± 0.07 mg KOH/g oil. These results show the great tendency of this oil for oxidation, due to the presence of components with many double bonds.

3.1. Camelina Sativa Oil Deacidification Using Alkali Six experiments were performed to remove FFA from three samples of Camelina sativa oil with initial acidity 3.35 mg KOH/g oil and three samples with 7.17 mg KOH/g oil. After deacidification with sodium hydroxide, the samples’ acid value decreased to ≈1 mg KOH/g, respectively, to ≈2.1 mg KOH/g., while the losses were about 18% for all the samples. Deacidification efficiency was around 82% for all samples, and 0.6 g/g oil was collected as wastewater.

3.2. Camelina Sativa Oil Deacidiﬁcation by Molecular Distillation Camelina sativa oil with 7.17 ± 0.07% mg KOH/g oil acidity was used. Thirty-nine experimental runs were performed to investigate the effects of evaporator temperature (seven experiments), wiper rolling speed (eight experiments), and feed ﬂow rate (20 experi- Sustainability 2021, 13, x FOR PEER REVIEW 7 of 19

3.2. Camelina Sativa Oil Deacidification by Molecular Distillation Camelina sativa oil with 7.17 ± 0.07% mg KOH/g oil acidity was used. Thirty-nine ex- Sustainability 2021, 13, 2818 perimental runs were performed to investigate the effects of evaporator temperature 7 of 18 (seven experiments), wiper rolling speed (eight experiments), and feed flow rate (20 experiments) on deacidification efficiency. The vacuum conditions of 0.1 Pa were maintained in the system, and condenser temperature at 19 °C for all experiments. FFA, to- ments) on deacidification efficiency. The vacuum conditions of 0.1 Pa were maintained in copherol, and MAG molecules, having smaller volume◦ than glycerides, are first vaporized at specifiedthe system, pressure and conditions condenser temperatureand migrate atto 19condenserC for all surface. experiments. The evaporation FFA, tocopherol, tem- and peratureMAG for molecules, Camelina sativa having oil smaller(as a mixture volume of FFA, than glycerides,TAG, DAG, are MAG, first and vaporized other compo- at specified nents)pressure was selected conditions at 150° and by migratemany experime to condenserntal trials surface. when The the evaporation first distillate temperature was col- for Camelina sativa lected (evaporation degreeoil (as abetween mixture 1% of FFA,and 10 TAG,%) at DAG, pressure MAG, 0.1 and Pa. otherThis temperature, components) was selected at 150◦ by many experimental trials when the first distillate was collected (evap- much lower than reported in literature [11–15], allowed us to reduce the heat consumption oration degree between 1% and 10%) at pressure 0.1 Pa. This temperature, much lower required by the evaporator, even if a high vacuum must be ensured. The wiper rolling than reported in literature [11–15], allowed us to reduce the heat consumption required by speed and feed flowrates were chosen according to the limits of the equipment. Deacidi- the evaporator, even if a high vacuum must be ensured. The wiper rolling speed and feed fication efficiency was greater than 90% for some operating conditions, no wastewater flowrates were chosen according to the limits of the equipment. Deacidification efficiency generated, and the oil losses are around 1–2%. Separated FFA can be valorized as valuable was greater than 90% for some operating conditions, no wastewater generated, and the oil products. losses are around 1–2%. Separated FFA can be valorized as valuable products.

3.2.1. 3.2.1.Effect Effectof Evaporator of Evaporator Temperature Temperature SevenSeven molecular molecular distillation distillation experiments experiments were wereperformed performed at different at different evaporator evaporator temperaturestemperatures between between 150 °C 150 to ◦180C to °C, 180 fixed◦C, fixedfeed feedflow flowrate (2 rate mL/min), (2 mL/min), wiper wiper rolling rolling speedspeed (300 rpm), (300 pressure rpm), pressure (0.1 Pa), (0.1 and Pa), conden andser condenser temperature temperature (19 °C). At (19 this◦C). lower At thispres- lower sure, thepressure, mixture the boiling mixture temperature boiling temperature was not too was high not and too TAG high could and TAG not degrade could not (for degrade a separation(for a at separation 200 °C, it at was 200 observed◦C, it was that observed compounds that compounds with lower with number lower of number carbons of ap- carbons pearedappeared due to the due glycerides to the glycerides degradation). degradation). During During the separation the separation process, process, light product light product rich inrich FFA in and FFA heavy and heavy product product rich in rich TAG in TAGwere wereseparated. separated. Increasing Increasing the evaporator the evaporator temperature,temperature, the FFA the FFAconcentration concentration in oil in decreased oil decreased from from 3.29% 3.29% at 150 at 150 °C ◦toC 1.61% to 1.61% at at180 180 ◦C °C (Figure(Figure 1a),1a), 90% 90% for for all all samples. samples. At At temperature greatergreater than than 170 170◦ °C,C, the the composition composition of FFA of FFAhad had a smaller a smaller change. change. The The composition composition of TAG of TAG in oil in was oil also was affected also affected by the evaporationby the evaporationtemperature, temperature, increasing increasing by 2% from by 2% 91.37% from at 91.37% 150 ◦C at to 150 93.34% °C atto 18093.34%◦C (Figure at 1801 °Ca). The (Figuredeacidification 1a). The deacidification efficiency increasedefficiency withincreased the temperature, with the temperature, having a having maximum a max- value of imum80.39% value of at 80.39% 180 ◦C (Figureat 180 °C1b). (Figure 1b).

Figure 1. Effect of evaporator temperature at constant flowrate (2 mL/min) and wiper rolling speed Figure(300 1. Effect rpm) of on evaporator (a) FFA and temperature TAG composition at consta innt deacidified flowrate (2 oil, mL/min) (b) deacidification and wiper rolling efficiency. speed (300 rpm) on (a) FFA and TAG composition in deacidified oil, (b) deacidification efficiency. 3.2.2. Effect of Wiper Rolling Speed Experimental studies were performed at five values between 250 and 350 rpm at two evaporator temperatures (160 ◦C and 170 ◦C), keeping the fix flow rate of 2 mL/min at pressure 0.1 Pa and condenser temperature 19 ◦C. As can be seen in Figure2, the wiper rolling element (model UIC GmbH, Alzenau, Germany) had a great influence in the deacidification process by molecular distillation due to its role of dispersing liquid on the evaporator surface in a thin film and liquid mixing. Sustainability 2021, 13, x FOR PEER REVIEW 8 of 19

3.2.2. Effect of Wiper Rolling Speed Experimental studies were performed at five values between 250 and 350 rpm at two evaporator temperatures (160 °C and 170 °C), keeping the fix flow rate of 2 mL/min at pressure 0.1 Pa and condenser temperature 19 °C. As can be seen in Figure 2, the wiper Sustainability 2021, 13, 2818 rolling element (model UIC GmbH, Alzenau, Germany) had a great influence in the dea- 8 of 18 cidification process by molecular distillation due to its role of dispersing liquid on the evaporator surface in a thin film and liquid mixing.

Figure 2. Effect of wiper rolling speed at constant flow rate (2 mL/min) and evaporation temperature Figure 2. Effect◦ of wiper rolling speed at constant flow rate (2 mL/min) and evaporation temperature (170(170 °C)C) onon (a) ( aFFA) FFA and and TAG TAG composition composition in indeacidified deacidified oil, oil, (b ()b deacidification) deacidification efficiency. efficiency. The increasing speed of wiper rolling decreased oil acidity from 2.46% to 0.88% at The increasing speed of wiper rolling decreased oil acidity from 2.46% to 0.88% at 170 ◦C (results presented in Figure2a) and from 3.12% to 0.89% at 160 ◦C. Additionally, 170 °C (results presented in Figure 2a) and from 3.12% to 0.89% at 160 °C. Additionally, deacidification efficiency showed a significant increase at high speeds (88.83% at 160 ◦C deacidification efficiency showed a significant increase at high speeds (88.83% at 160 °C and 91.75% at 170 ◦C). TAG concentration showed a moderate increase from almost 91.5% and 91.75% at 170 °C). TAG concentration showed a moderate increase from almost 91.5% at 250 rpm to 93% at 350 rpm with small differences at the two evaporation temperatures at 250 rpm to 93% at 350 rpm with small differences at the two evaporation temperatures studied. Under the same conditions, calculated deacidification efficiency showed an studied.increase Under with the same 28% (forconditions, 160 ◦C) andcalculated 20% (for deacidification 170 ◦C), for anefficiency increase showed in the wiper an in- rolling crease rotationswith 28% with (for 100 160 rpm, °C) dueand to 20% a uniform (for 170 oil °C), distribution for an increase on the evaporator in the wiper walls rolling facilitating rotationsthe with evaporation 100 rpm, (Figure due to2 ab). uniform oil distribution on the evaporator walls facilitating the evaporation (Figure 2b). 3.2.3. Effect of Feed Flow Rate 3.2.3. EffectThese of Feed studies Flow wereRate performed at an evaporator temperature of 170 ◦C and different Thesevalues studies of feed were flowrates performed (2 mL/min, at an evaporator 4 mL/min temperature and 6 mL/min) of 170 °C with and wiper different speed at values250 of feed rpm, flowrates300 rpm, and(2 mL/min, 350 rpm 4 atmL/min constant and pressure 6 mL/min) (0.1 Pa) with and wiper condenser speed temperatureat 250 rpm, 300 rpm,◦ and 350 rpm at constant pressure (0.1 Pa) and condenser temperature (19 Sustainability 2021, 13, x FOR PEER REVIEW(19 C). The TAG composition decreased and FFA composition in bottom product increased9 of 19 °C). Thewhen TAG the composition flow rates decreased increased. and At FFA 6 mL/min, composition the lowestin bottom FFA product could beincreased greater than when 2.5%the flow (Figure rates3a). increased. At 6 mL/min, the lowest FFA could be greater than 2.5% (Figure 3a).

Figure 3. Effect of feed flow rate at constant temperature (170 ◦C) and wiper speed (300 rpm) on (a) FigureFFA and 3. Effect TAG compositionof feed flow rate in deacidified at constant oil, temperature (b) deacidification (170 °C) efficiency.and wiper speed (300 rpm) on (a) FFA and TAG composition in deacidified oil, (b) deacidification efficiency.

At low wiper speed, FFA content did not vary too much, but at high values, this difference became significant. This was determined by the fact that the feed flow rate influenced the system heat transfer: more energy for evaporation was needed if flow rate was increasing. In Figure 3b, the influence of feed flow rate versus deacidification efficiency is presented at different wiper rolling speeds. It was noticed that, at low speeds, the decrease was less than 5%, but at high speed, the deacidification efficiency decreased by 10% (Figure 3b).

3.3. Design of Experiment for Camelina Sativa Oil Deacidification Process 3.3.1. Fitting Models Responses as FFA content in the bottom product (Y1), deacidification efficiency (Y2), and TAG concentration in the bottom product (Y3) were fitted by a second-order model with regression coefficients presented in Table 4, using a Box–Behnken design of experimental data. The fitted response surfaces were calculated with Equations (7)–(9):

Y = 2.510 − 0.270X − 0.296X + 0.499X + 0.155XX + 0.363XX + 0.020XX + 0.201X + 0.039X (7) − 0.361X

Y = 75.410 + 5.570X + 8.939X − 3.966X − 1.903XX − 2.015XX + 2.328XX − 2.298X + 1.735X − 0.090X (8)

Y = 91.412 + 0.671X + 0.401X − 0. 963X − 0.010XX − 0.353XX + 0.157XX + 0.074X (9) − 0.381X + 0.426X

Table 4. Regression coefficients for proposed models.

Regress. Y1 (FFA) Y2 (DE) Y3 (TAG) Factor Coef. Coef. p-Value Coef. p-Value Coef. p-Value Intercept β0 2.510 <0.001 75.410 <0.001 91.412 <0.001 X1 β1 −0.270 <0.001 5.570 <0.001 0.671 <0.001 X12 β7 0.201 0.005 −2.298 <0.001 0.074 0.273 X2 β2 −0.296 <0.001 8.939 <0.001 0.401 0.001 X22 β8 0.039 0.334 1.735 <0.001 −0.381 0.003 X3 β3 0.499 <0.001 −3.966 <0.001 −0.963 <0.001 X32 β9 −0.361 0.001 −0.090 0.399 0.426 0.002 Sustainability 2021, 13, 2818 9 of 18

At low wiper speed, FFA content did not vary too much, but at high values, this difference became significant. This was determined by the fact that the feed flow rate influenced the system heat transfer: more energy for evaporation was needed if flow rate was increasing. In Figure3b, the influence of feed flow rate versus deacidification efficiency is presented at different wiper rolling speeds. It was noticed that, at low speeds, the decrease was less than 5%, but at high speed, the deacidification efficiency decreased by 10% (Figure3b).

3.3. Design of Experiment for Camelina Sativa Oil Deacidiﬁcation Process 3.3.1. Fitting Models

Responses as FFA content in the bottom product (Y1), deacidification efficiency (Y2), and TAG concentration in the bottom product (Y3) were fitted by a second-order model with regression coefficients presented in Table4, using a Box–Behnken design of experimental data. The fitted response surfaces were calculated with Equations (7)–(9):

2 2 2 YFFA = 2.510 − 0.270X1 − 0.296X2 + 0.499X3 + 0.155X1X2 + 0.363X2X3 + 0.020X1X3 + 0.201X1 + 0.039X2 − 0.361X3 (7)

2 2 2 YDE = 75.410 + 5.570X1 + 8.939X2 − 3.966X3 − 1.903X1X2 − 2.015X2X3 + 2.328X1X3 − 2.298X1 + 1.735X2 − 0.090X3 (8) 2 2 2 YTAG = 91.412 + 0.671X1 + 0.401X2 − 0.963X3 − 0.010X1X2 − 0.353X2X3 + 0.157X1X3 + 0.074X1 − 0.381X2 + 0.426X3 (9)

Table 4. Regression coefﬁcients for proposed models.

Regress. Y (FFA) Y (DE) Y (TAG) Factor 1 2 3 Coef. Coef. p-Value Coef. p-Value Coef. p-Value

Intercept β0 2.510 <0.001 75.410 <0.001 91.412 <0.001 X1 β1 −0.270 <0.001 5.570 <0.001 0.671 <0.001 2 X1 β7 0.201 0.005 −2.298 <0.001 0.074 0.273 X2 β2 −0.296 <0.001 8.939 <0.001 0.401 0.001 2 X2 β8 0.039 0.334 1.735 <0.001 −0.381 0.003 X3 β3 0.499 <0.001 −3.966 <0.001 −0.963 <0.001 2 X3 β9 −0.361 0.001 −0.090 0.399 0.426 0.002 X1X2 β4 0.155 0.013 −1.903 <0.001 −0.010 0.875 X1X3 β6 0.020 0.610 2.328 <0.001 0.157 0.058 X2X3 β5 0.363 0.001 −2.015 <0.001 −0.353 0.004 FFA = Free Fatty Acids, DE = deacidiﬁcation efﬁciency, TAG = triglycerides.

For each response, all coefficients, and their p-value with 95% confidence interval were computed to validate the significance of regression models. The regression model is significant at 5% level if the p-value is smaller than 0.05. From Table4, it can be observed that some terms did not have a significant effect: quadratic X2 and interaction X1X3 for Y1, only quadratic X3 for Y2 and quadratic X1, and interaction X1X2 for Y3. In Table5, the experimental and data predicted with proposed second-order models are presented. It was observed that close correlations existed between the data. Also, the good correlation can be noticed in Figure4 when predicted data are plotted versus experimental data for all three ◦ responses, all points being placed near a 45 line, especially for Y2.

Table 5. Experimental vs predicted data with second-order model.

X1 X2 X3 Y1 (%FFA) Y2 (%DE) Y3 (%TAG) Run ◦ ( C) (rpm) (mL/min) exp pred exp pred exp pred 1 160 250 4 3.37 3.47 58.37 58.44 89.94 90.02 2 180 250 4 2.46 2.62 73.33 73.38 91.61 91.39 3 160 350 4 2.73 2.57 80.17 80.12 90.62 90.85 4 180 350 4 2.44 2.34 87.52 87.45 92.25 92.17 Sustainability 2021, 13, 2818 10 of 18

Table 5. Cont. Sustainability 2021, 13, x FOR PEER REVIEW 10 of 19

X1 X2 X3 Y1 (%FFA) Y2 (%DE) Y3 (%TAG) Run ◦ ( C) (rpm) (mL/min) exp pred exp pred exp pred 5X1X2 160β4 3000.155 20.013 2.13−1.903 2.14<0.001 73.92 −0.010 73.75 0.87592.62 92.36 6X1X3 180β6 300 0.020 20.610 1.61 2.328 1.56<0.001 80.39 0.157 80.23 0.05893.34 93.39 7X2X3 160β5 3000.363 60.001 3.05−2.015 3.10<0.001 61−0.353 61.16 0.00490.17 90.12 FFA 8= Free Fatty 180 Acids, DE 300 = deacidific 6ation efficiency, 2.61 TAG 2.60= triglycerides. 76.78 76.95 91.52 91.78 9 170 250 2 2.46 2.35 69.96 70.07 91.49 91.67 10For each 170 response, 350 all coefficients, 2 and 0.88their p-value 1.03 with 91.7595% confidence 91.98 interval93.14 93.17 were11 computed 170 to validate 250 the significance 6 of 2.77 regression 2.62 models. 66.39 The regression 66.17 model90.48 is 90.45 significant12 at 170 5% level 350if the p-value 6 is smaller 2.64 than 0.05. 2.75 From Table 80.12 4, it can 80.01 be observed90.72 90.54 that13 some terms 170 did not 300 have a significant 4 effect: 2.51 quadratic 2.51 X2 75.34and interaction 75.41 X1X391.34 for 91.41 Y1, 14only quadratic 170 X3 300for Y2 and quadratic 4 X1, 2.45 and interaction 2.51 X1X2 75.32 for Y3. 75.41 In Table91.46 5, the 91.41 experimental15 170 and data 300 predicted with 4 propos 2.53ed second-order 2.51 75.35models are 75.41 presented.91.60 It 91.41 was16 observed 170 that close 300 correlations 4 existed between 2.44 the 2.51 data. Also, 75.34 the good 75.41 correlation91.35 91.41 can 17be noticed 170 in Figure 300 4 when predicted 4 da 2.62ta are plotted 2.51 versus 75.33 experimental 75.41 data91.31 for 91.41 all three responses, all points being placed near a 45° line, especially for Y2.

(a) (b) (c) Figure 4. Predicted versus experimental data for regression quadratic models. (a) Y1 (FFA), (b) Y2 Figure 4. Predicted versus experimental data for regression quadratic models. (a)Y1 (FFA), (b)Y2 (DE), (c) Y3 (TAG). (DE), (c)Y3 (TAG). Table 5. Experimental vs predicted data with second-order model. 3.3.2. Statistical Validation of Model X1 X2 X3 Y1 (%FFA) Y2 (%DE) Y3 (%TAG) RunTable 6 shows the analysis of variance (ANOVA) for all the responses investigated in this study. The(°C models,) (rpm) linear, (mL/min) quadratic, exp andpred interaction exp terms pred of the threeexp independent pred variables (temperature,1 160 speed,250 and flow 4 rate), 3.37 lack 3.47of fit (model 58.37 error),58.44 and pure89.94 error 90.02 (replicates error) were2 evaluated 180 250 according 4 to their 2.46 degree 2.62 of freedom, 73.33 sum73.38 of squares, 91.61 mean 91.39 square, F-value, p-value,3 and 160 model350 coefficient 4 of 2.73 determination 2.57 R 80.172. A model80.12 is good90.62 if it has90.85 a high F-value, low4 p-value, 180 insignificant350 4 lack of 2.44 fit, and 2.34 high R2 87.52. The results87.45 of this92.25 analysis 92.17 are presented 5 160 300 2 2.13 2.14 73.92 73.75 92.62 92.36 in Table6. For the FFA content (Y 1), the ANOVA analysis of the quadratic model showed 6 180 300 2 1.61 1.56 80.39 80.23 93.34 93.39 that the F-value of 21.156 implies that the model is significant at p < 0.001. The coefficient of 7 160 300 6 3.05 3.10 61 61.16 90.17 90.12 determination R2 is 0.965, measuring how properly experimental data are approximated the 8 180 300 6 2.61 2.60 76.78 76.95 91.52 91.78 regression model. The lack of fit was not significant (p > 0.05) and indicated that the model 9 170 250 2 2.46 2.35 69.96 70.07 91.49 91.67 prediction is adequate. Examination of the coefficients for the first response indicated that 10 170 350 2 0.88 1.03 91.75 91.98 93.14 93.17 the11 linear 170 terms 250 as evaporator 6 temperature, 2.77 2.62 wiper 66.39 speed, 66.17 and flow90.48 rate and90.45 the interaction terms12 between 170 wiper350 speed 6 and 2.64 flow rate 2.75 were highly80.12 significant80.01 90.72 with higher90.54 F-value and lower13 p-value. 170 Quadratic300 terms 4 of 2.51 evaporator 2.51 temperature 75.34 75.41 and flow91.34 rate and91.41 interaction term between14 temperature170 300 and speed 4 were 2.45 significant, 2.51 while 75.32 quadratic75.41 term91.46 of speed91.41 and interaction term15 between 170 temperature300 4 and flow 2.53 rate 2.51 were not 75.35 significant 75.41 with 91.60 low F-value91.41 and p > 0.05. For16 the deacidification 170 300 efficiency 4 2.44 (Y2), the 2.51 ANOVA 75.34 analysis 75.41 showed 91.35 that a greater91.41 F-value of 2 2123.1517 implies 170 that300 the model 4 is 2.62 significant 2.51 at p < 75.33 0.001. The75.41 coefficient 91.31 of determination91.41 R was 0.999 and all the linear, quadratic, and interaction terms were significant (p < 0.05), except 3.3.2.the quadratic Statistical effectValidation of flow of Model which was insignificant at p > 0.05. To determine if the models were compatible with experimental data, lack of fit test was calculated from residuals of fitting. The model was adequate, as indicated by the error analysis that showed insignificant lack of Sustainability 2021, 13, 2818 11 of 18

fit with p > 0.05. In case of the third response, TAG concentration (Y3), the obtained results showed that the model was significant with a F-value of 29.522 and p < 0.001. The coefficient of determination R2 was 0.974. All the linear terms were highly significant with high F-value and low p-value, while the quadratic term of evaporator temperature and the interaction term between temperature and speed were insignificant with low F-value and p > 0.05.

Table 6. Analysis of variance (ANOVA) of all responses.

Response DF SS MS F-Value p-Value Model 9 4.592 0.510 21.156 <0.001 X1 1 0.583 0.583 111.086 <0.001 X2 1 0.702 0.702 133.736 <0.001 X3 1 1.990 1.990 379.050 <0.001 2 X1 1 0.171 0.171 32.482 0.005 2 X2 1 0.006 0.006 1.204 0.334 2 X3 1 0.549 0.549 104.663 0.001 Y1 X1X2 1 0.096 0.096 18.305 0.013 (FFA%) X1X3 1 0.002 0.002 0.305 0.610 X2X3 1 0.526 0.526 109.119 0.001 Residual 7 0.169 0.024 - - Lack of fit 3 0.148 0.049 6.256 0.054 Pure error 4 0.021 0.005 - - R 0.982 R2 0.965 Adjusted R2 0.919 Model 9 1098.93 122.10 2123.15 <0.001 X1 1 248.199 248.199 6471.95 <0.001 X2 1 639.210 639.210 16667.80 <0.001 X3 1 125.849 125.849 3281.59 <0.001 2 X1 1 22.225 22.225 579.54 <0.001 2 X2 1 12.675 12.675 330.50 <0.001 2 X3 1 0.034 0.034 0.89 0.399 Y2 X1X2 1 14.478 14.478 377.52 <0.001 (DE) X1X3 1 21.669 21.669 565.03 <0.001 X2X3 1 16.241 16.241 423.49 <0.001 Residual 7 0.403 0.058 - - Lack of fit 3 0.249 0.083 2.17 0.235 Pure error 4 0.153 0.038 - - R 0.999 R2 0.999 Adjusted R2 0.999 Model 9 14.234 1.582 29.522 <0.001 X1 1 3.605 3.605 252.601 <0.001 X2 1 1.288 1.288 90.260 0.001 X3 1 7.411 7.411 519.359 <0.001 2 X1 1 0.023 0.023 1.616 0.273 2 X2 1 0.611 0.611 42.831 0.003 2 X3 1 0.766 0.766 53.672 0.002 Y3 X1X2 1 0.001 0.001 0.028 0.875 (TAG%) X1X3 1 0.099 0.099 6.953 0.058 X2X3 1 0.497 0.497 34.830 0.004 Residual 7 0.375 0.054 - - Lack of fit 3 0.318 0.106 7.426 0.041 Pure error 4 0.057 0.014 - - R 0.987 R2 0.974 Adjusted R2 0.941 Where DF = degree of freedom, SS = sum of squares, MS = mean square. Sustainability 2021, 13, 2818 12 of 18

3.3.3. Optimization by Using Response Surface Methodology This optimization method was used for Camelina sativa oil deacidification by molecular distillation process to locate one point or region/regions on factor surfaces, where fitted responses were acceptable (optimal) values. Response surfaces were computed for factor ranges between minimum and maximum values of fitted responses obtained with second- order model. The shape of these surfaces gave information about regions where responses had optimal values and the ranges of optimal factors that affected these responses. As can be seen from Y1 surfaces (Figure5a), FFA composition should be at a minimum value in regions of higher X1 and X2 and lower X3.

Figure 5. Response surfaces for Y1,Y2, and Y3 based on: (a)X1X2 effects, X3 = ct, (b)X1X3 effects, X2 = ct, (c)X2X3 effects, X1 = ct. These factors will have a positive impact on FFA composition (Y1 ≈ 1% FFA). Y2 surfaces had maximum regions (90–95%), identified in Figure5b also in the same regions, for higher X1and X2 and lower X3. In Figure5c, Y 3 response surfaces in the same regions for maximum value of 93.5% are presented. First response (Y1-FFA) must be minimized while second (Y2-DE) and third (Y3-TAG concentration) responses must be maximized. 2D plots were computed to identify optimal values of responses when each factor had a variation between −1 and 1, and the other factors had constant values (from ranges identified on response surfaces). In Figure6a, three profiles of response Y 1 affected by X1, X2 and X3 are presented, these profiles showing minimum values. Sustainability 2021, 13, x FOR PEER REVIEW 14 of 19 Sustainability 2021, 13, 2818 13 of 18

Figure 6. Optimal responses: (a)X effects, (b)X effects, (c)X effects. 1 2 3 Profiles presented in Figure6b,c represented the responses of Y and Y affected by Figure 6. Optimal responses: (a) X1 effects, (b) X2 effects, (c) X3 effects. 2 3 the same factors. These profiles presented maximum values. All three response profiles Tablepresented 7. Desirability the optimal function values parameters. at X1 = 0.34629, X2 = 1, and X3 = −1. These values were in the regions identified in the regions from response surfaces. Optimization Smallest Medium Target For all responses,Response individual desirability functions were computed with values be- tweenObjective 0 and 1, where 0 wasValue/Desirability for an undesirable Value/Desirability response (response Value/Desirability is greater than a target 0.88 2.125 3.37 valueMinimization for minimum Y1(FFA) point or less than the smallest value for maximum) and 1 indicated a completely desirable response (response1 is less than the0.5 smallest value for0 minimum point 58.37 75.06 91.75 orMaximization greater than aY2(DE) target value for maximum point). Intermediate values of responses between smallest value and target value0 represented desirable0.5 responses. Smallest,1 medium, 90.04 91.70 93.36 andMaximization target values Y3(TGA) for desirability functions are presented in Table7, considering a linear variation for intermediate values. 0 0.5 1

ComputingTable the 7. individualDesirability desirability function parameters. functions with STATISTICA© for factor values between −1 and 1; their profiles are presented in Figure 7. Smallest Medium Target Optimization Objective Response Value/Desirability Value/Desirability Value/Desirability 0.88 2.125 3.37 Minimization Y (FFA) 1 1 0.5 0 58.37 75.06 91.75 Maximization Y (DE) 2 0 0.5 1 90.04 91.70 93.36 Maximization Y (TGA) 3 0 0.5 1

Computing the individual desirability functions with STATISTICA© for factor values between −1 and 1; their proﬁles are presented in Figure7. SustainabilitySustainability 2021, 132021, x FOR, 13, PEER 2818 REVIEW 15 of 1914 of 18

Figure 7. Optimal desirability for: (a)X1 factor, (b)X2 factor, (c)X3 factor. Figure 7. Optimal desirability for: (a) X1 factor, (b) X2 factor, (c) X3 factor. In Table8, the optimal values of responses and factors are centralized. Final desirability Infunction, Table 8, definedthe optimal as geometric values of mean response of individuals and factors desirability are centralized. functions, Final was desira- 0.9826 for all bility function,factors combined. defined as These geometric optimal mean values of ofindividual coded factors desirability corresponded functions, to optimal was 0.9826 operating for allconditions factors combined. such as: These evaporator optimal temperature values of coded of 173.5 factors◦C, wiper corresponded rolling speed to optimal of 350 rpm, operatingand conditions a feed flow such rate ofas: 2 evaporator mL/min for temperature the deacidification of 173.5 °C, process wiper of rollingCamelina speed sativa of oil by 350 rpm,molecular and a feed distillation. flow rate of At 2 these mL/min conditions, for the deacidification separation parameters process of for Camelina this process sativa were oil by minimummolecular composition distillation. ofAt FFA these in conditions, bottom product separation of 1.007% parameters FFA, maximum for this deacidification process were minimumefficiency ofcomposition 92.167%, and of FFA maximum in bottom concentration product of of1.007% TAG inFFA, bottom maximum product deacid- of 93.360% ificationTAG, efficiency with a desirabilityof 92.167%, ofand 0.9826. maximum concentration of TAG in bottom product of 93.360% TAG, with a desirability of 0.9826. To validateTable 8. Optimal the model factors, prediction, optimal responses,two paralle andl trials desirability. were performed in the optimal operating conditions. FFA concentration of 1.15 ± 0.2%, a TAG concentration of 92.85 ± Optimization Objective Optimal Response Optimal Factors Individual Desirability Desirability 0.05%, with a deacidification efficiency of 91.30 ± 0.1% were obtained, which is in good agreement with theoretical prediction.X1 = 0.34629 0.9488 Minimization Y1(%FFA) = 1.007 X2 = 0.99996 1.0000 0.9826 − Table 8. Optimal factors, optimal responses,X3 = 1 and desirability. 1.0000 X1 = 0.34629 0.9488 Optimization Individual Maximization Y2(%DE) =Optimal 92.167 ResponseX2 = 0.99996 Optimal Factors 1.0000 Desirability0.9826 Objective X3 = −1 1.0000Desirability

X1 = 0.34629X1 = 0.34629 0.9488 0.9488 Maximization MinimizationY3(%TGA) = Y1(%FFA) 93.36 = 1.007X2 = 0.99996 X2 = 0.99996 1.0000 1.0000 0.98260.9826 X3 = −1X3 = −1 1.00001.0000 X1 = 0.34629 0.9488 To validate the model prediction, two parallel trials were performed in the optimal op- Maximization Y2(%DE) = 92.167 X2 = 0.99996 1.0000 0.9826 erating conditions. FFA concentration of 1.15 ± 0.2%, a TAG concentration of 92.85 ± 0.05%, X3 = −1 1.0000 with a deacidification efficiency of 91.30 ± 0.1% were obtained, which is in good agreement with theoretical prediction. X1 = 0.34629 0.9488 Maximization Y3(%TGA) = 93.36 X2 = 0.99996 1.0000 0.9826 3.4. Process Design, Economic and EnvironmentalX3 = −1 Analysis 1.0000 In Figure8, the process flowsheets for two deacidification processes of Camelina sativa 3.4. Processoil are Design, presented: Economic ODAW—conventional and Environmental Analysis method with alkali (Figure8a) and ODMD— Inmolecular Figure 8, distillationthe process (Figureflowsheets8b). Afor 100 two kg/batch deacidification of crude processes oil was fed of Camelina in each process sativa and oil are92.5 presented: kg/batch ODAW—conventional of refined oil was obtained method from with ODMD alkali process(Figure with8a) and 12.8% ODMD— more than in molecularODAW distillation process. (Figure 8b). A 100 kg/batch of crude oil was fed in each process and 92.5 kg/batchThe of annual refined number oil was ofobtained batches fr wasom ODMD different process for these with two 12.8% processes, more than due in to the different operational time, 15 h for ODMD, and 50 h for the other process. ODAW process ODAW process. involved a reaction step, free fatty acids reacted with alkali to form soap, which retained 5% of crude vegetable oil. Three filtering and washing steps were needed to removed soaps and the 60 kg/batch of resulting wastewater needed to be treated. ODMD process involved a vacuum step and heating and cooling agents to assure evaporator temperature at 173.5 ◦C (to vaporize FFA) and 19 ◦C for cooling zone (to condense FFA molecules). Different tanks were used for raw materials and products. Sustainability 2021, 13, 2818 15 of 18 Sustainability 2021, 13, x FOR PEER REVIEW 16 of 19

o T = 25 C T4 100 kg/batch P = 0.001 bar T = 174oC T = 25oC HOT 100 kg/batch FFA 7.37%wt. P = 1 bar Utility P = 1 bar 30 kg/batch T = 25oC o VP1 FFA 7.37%wt. T = 25 C o P = 1 bar T = 25oC P = 1 bar T = 25 C C1 H1 T = 19oC CRUDE P = 1 bar WATER P = 1 bar Q = 32 kW/batch H2 OIL T1 P = 1 bar Q = 22.5 kW/batch Q = 38 kW/batch T = 120oC T = 155oC 2 kg/batch T4 82 kg/batch P = 1 bar P = 0.001 bar FFA 2.02%wt T=70oC DMU ALKALI P=1 bar F3 T2 OIL SOLUTION 1.5kg/batch losses T = 174oC T6 F2 P = 1 bar COLD T5 R1 Utility o o T = 25 C WASTEWATER T = 29 C 10 kg/batch o P = 1 bar TREATMENT/ T = 155 C P = 1 bar F1 . SOLVENT P = 1 bar RECOVERY 92.5 kg/batch Q = 0.5 kW/batch FFA 1.07%wt. T- Tank T- Tank T3 WT1 T = 25oC DMU-Distilation unit SOLVENT R-Autoclave Q = 4 kW/batch P = 1 bar 6 kg/batch VP-Vacuum pump F-Filter 60 kg/batch FFA 86%wt. H-Heater CRUDE o WT-treatment unit OIL T = 25 C C-Cooler OIL T1 T2 DIST(FFA) T3 P = 1 bar

(a) (b)

FigureFigure 8.8. DeacidiﬁcationDeacidification process ﬂowsheetflowsheet ( a) ODAW-alkali process, ( b)) ODMD-molecular ODMD-molecular distillation distillation process. process.

TheThe mainannual advantages number of of batches the ODMD was different process arefor thethese reduction two processes, of crude due oil lossesto the fromdif- 18%ferent to operational 1.5%, and no time, wastewater 15 h for ODMD, generation. and For50 h this for process,the other heating/cooling/vacuum process. ODAW process systemsinvolvedcosts a reaction are increased. step, free fatty In Table acids9 ,reacted raw materials, with alkali utility to form consumptions, soap, which retained and the production5% of crude cost vegetable estimation oil. forThree both filtering processes and are washing presented. steps were needed to removed soaps and the 60 kg/batch of resulting wastewater needed to be treated. ODMD process Tableinvolved 9. Economic a vacuum assessment step and for heCamelinaating and sativa coolingoil deacidification. agents to assure evaporator temperature at 173.5 °C (to vaporize FFA) and 19 °C for cooling zone (to condense FFA molecules). Alkali Washing Process Molecular Distillation Process Different tanks were used for raw materials and products. (ODAW) (ODMD) The main advantages of the ODMD process are the reduction of crude oil losses from Total capital investment, $18% to 1.5%, and no wastewater 19,000.00 generation. For this process, heating/cooling/vacuum 33,600.00 Plant life, yearssystems costs are increased. In 10.00 Table 9, raw materials, utility consumptions, 10.00 and the pro- Crude oil consumption, kg/kg deacidified oil 1.22 1.08 Operational time, hduction cost estimation for both 50 processes are presented. 15 Raw materials, $/kg deacidified oil 1.82 1.30 Crude oil Table 9. Economic assessment for 1.46Camelina sativa oil deacidification. 1.30 Washing water 0.18 0.00 Alkali Washing Process Molecular Distillation Process Alkaline solution 0.05 0.00 Solvent 0.12(ODAW) (ODMD) 0.00 TotalUtilities, capital $/kg investment, deacidified oil $ 0.06 19,000.00 33,600.00 0.81 PlantHeating life, agent years 0.00 10.00 10.00 0.58 CrudeCooling oil agent consumption, kg/kg deacidified oil 0.00 1.22 1.08 0.22 Energy 0.06 0.01 OperationalWastewater treatment, time, h $/kg deacidified oil 1.10 50 0.00 15 RawVariable materials, costs,$/kg $/kg deacidified deacidified oil oil 2.97 1.82 1.30 2.10 CrudeDirect oil fixed costs,$/kg deacidified oil 0.261.46 1.30 0.15 WashingTotal cash water cost, $/kg deacidified oil 3.230.18 0.00 2.25 ADepreciation,lkaline solution $/kg deacidified oil 0.150.05 0.00 0.09 Cost of production, $/kg deacidified oil 3.39 2.34 Solvent 0.12 0.00 Utilities, $/kg deacidified oil 0.06 0.81 Heating agent Production cost was evaluated0.00 as a contribution of variable costs0.58 (equipment, raw Cooling agent materials, utilities, and treatment0.00 costs), fixed costs (operating, non-operating0.22 and admin- Energy istration labor costs, land, and others)0.06 and depreciation costs (Figure0.019 ). Raw material specific costs ($/kg deacidified oil) were 40% higher for the alkali process due to the Wastewater treatment, $/kg deacidified oil 1.10 0.00 consumption of alkali solutions, wastewaters, and solvents. Also, the variable costs for Variable costs,$/kg deacidified oil 2.97 2.10 Direct fixed costs,$/kg deacidified oil 0.26 0.15 Sustainability 2021, 13, x FOR PEER REVIEW 17 of 19

Total cash cost, $/kg deacidified oil 3.23 2.25 Depreciation, $/kg deacidified oil 0.15 0.09 Cost of production, $/kg deacidified oil 3.39 2.34

Production cost was evaluated as a contribution of variable costs (equipment, raw Sustainability 2021, 13, 2818 materials, utilities, and treatment costs), fixed costs (operating, non-operating and admin-16 of 18 istration labor costs, land, and others) and depreciation costs (Figure 9). Raw material specific costs ($/kg deacidified oil) were 40% higher for the alkali process due to the consumption of alkali solutions, wastewaters, and solvents. Also, the variable costs for the alkali theprocess alkali were process influenced were inﬂuenced by treatment. by treatment. For the Formolecular the molecular distillation distillation process, process, the higher the highercontribution contribution of production of production costs was costs gi wasven by given raw by materials raw materials and utility and utilitycosts. costs.

FigureFigure 9.9.Production Production costscosts componentscomponents forforCamelina Camelina sativa sativaoil oil deacidiﬁcation. deacidification.

4.4. ConclusionsConclusions TheThe efficiency efficiency of of the theCamelina Camelina sativa sativaoil oil deacidification deacidification process process by molecularby molecular distillation distilla- wastion determined was determined by combining by combining experimental experimental studies studies (to identify (to identify operating operating conditions) conditions) with statisticalwith statistical analysis analysis (for optimal (for optimal conditions) conditions) and technical-economicand technical-economic analysis analysis (to evaluate (to eval- productionuate production costs). costs). The results The results were compared were compared with a classical with a process,classical usingprocess, alkali using solutions alkali tosolutions remove to FFA. remove Experimental FFA. Experimental studies for deacidificationstudies for deacidification of Camelina of sativa Camelinaoil with sativa 7.67% oil FFAwith using 7.67% both FFA methods using both led tomethods a process led efficiency to a process over efficiency 80% and finalover FFA80% contentand final lower FFA thancontent 1%, lower but the than conventional 1%, but the process conventional generated process wastewater generated with wastewater soap and with 18% oilsoap loss. and 18% Molecularoil loss. distillation involves the mixture evaporation in vacuum conditions, and trial experimentsMolecular distillation were needed involves to obtain the operationmixture evaporation parameters. in Changing vacuum valuesconditions, of some and operatingtrial experiments conditions, were such needed as evaporator to obtain temperature,operation parameters. wiper rolling Changing speed, values and feed of some flow rateoperating in a specified conditions, range, such led as to evaporator a modification temperature, of deacidification wiper rolling efficiency speed, and with feed 35.44%. flow Moreover,rate in a specified 88% of FFArange, were led removedto a modification from oil, of while deacidification TAG composition efficiency in with refined 35.44%. oil variesMoreover, by 8%. 88% Optimal of FFA were operation removed conditions from oil, were while performed TAG composition using DOE, in refined formulating oil var- aies Box–Behnken by 8%. Optimal design operation with three conditions factors were and performed three responses: using DOE, evaporator formulating temperature a Box– was 173.5 ◦C, wiper speed was 350 rpm, and feed flow rate was 2 mL/min, evaluated Behnken design with three factors and three responses: evaporator temperature was 173.5 for a process desirability of 0.9826. In these conditions, the deacidification efficiency °C, wiper speed was 350 rpm, and feed flow rate was 2 mL/min, evaluated for a process was 92.167%, FFA composition in deacidified oil was 1.007%, and TAG concentration desirability of 0.9826. In these conditions, the deacidification efficiency was 92.167%, FFA was 93.36%. The technical and economic analysis accomplished for both deacidification composition in deacidified oil was 1.007%, and TAG concentration was 93.36%. The tech- processes (ODAW and ODMD), for 100 kg/batch Camelina sativa oil in optimal conditions, nical and economic analysis accomplished for both deacidification processes (ODAW and allows the evaluation of processes sustainability: raw materials and utility consumption ODMD), for 100 kg/batch Camelina sativa oil in optimal conditions, allows the evaluation per kg of deacidified Camelina sativa oil (1.22 kg vs. 1.08 kg and 1 kWh vs. 0.25 kWh), of processes sustainability: raw materials and utility consumption per kg of deacidified wastewater generation (0.73 kg vs. 0 kg), and production costs ratio (45% higher for Camelina sativa oil (1.22 kg vs. 1.08 kg and 1 kWh vs. 0.25 kWh), wastewater generation ODAW process). The results showed that using ODMD method some drawbacks of oil deacidification processes can be removed: the method can be applied for oils with different acidities and different compositions, oil losses are minimized, and wastewater treatment are reduced.

Author Contributions: Conceptualization, N.G.¸S.,P.I. and I.C.; Formal analysis, N.G.¸S.and P.I.; Investigation, N.G.¸S.,P.I. and V.P.; Methodology, N.G.¸S.,P.I., V.P., I.C. and N.D.I.; Supervision, V.P. and I.C.; Validation, V.P. and N.D.I.; Visualization, I.C.; Writing—original draft, N.G.¸S., P.I., I.C. and N.D.I.; Writing—review and editing, P.I. All authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc. via emails from our Sustainability 2021, 13, 2818 17 of 18

system or assigned Assistant Editor. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical considerations. Acknowledgments: The authors gratefully acknowledge the financial support of the European Commission through the European Regional Development Fund and of the Romanian state budget, under the grant agreement POC P-37-449 (acronym ASPiRE). Conflicts of Interest: The authors declare no conflict of interest.

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