Kinetic behavior, mathematical modeling, and economic evaluation of extracts obtained by supercritical fluid extraction from defatted assaí waste

Accepted Manuscript Title: Kinetic behavior, mathematical modeling, and economic evaluation of extracts obtained by supercritical fluid extraction from defatted assa´ı waste ´ Authors: Adina L. Santana, Juliana Q. Albarelli, Diego T. Santos, Renato Souza, N´elio T. Machado, Marilena E. Ara´ujo, M. Angela A. Meireles PII: DOI: Reference:

S0960-3085(17)30131-1 https://doi.org/10.1016/j.fbp.2017.10.006 FBP 916

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Food and Bioproducts Processing

Received date: Revised date: Accepted date:

2-8-2017 22-9-2017 19-10-2017

´ Please cite this article as: Santana, Adina L., Albarelli, Juliana Q., Santos, Diego T., Souza, Renato, Machado, N´elio T., Ara´ujo, Marilena E., Meireles, M.Angela A., Kinetic behavior, mathematical modeling, and economic evaluation of extracts obtained by supercritical fluid extraction from defatted assa´ı waste.Food and Bioproducts Processing https://doi.org/10.1016/j.fbp.2017.10.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Kinetic behavior, mathematical modeling, and economic evaluation of extracts obtained by supercritical fluid extraction from defatted assaí waste

ÁDINA L. SANTANA1*, JULIANA Q. ALBARELLI1, DIEGO T. SANTOS1, RENATO SOUZA2, NÉLIO T. MACHADO2, MARILENA E. ARAÚJO2, M. ANGELA A. MEIRELES1*

1

LASEFI/DEA/FEA (School of Food Engineering)/UNICAMP (University of Campinas) Cidade Universitária “Zeferino Vaz”, Rua Monteiro Lobato, 80, Campinas 13083-862, Brazil

2

Federal University of Pará. R. Augusto Correa, s/n, Guamá, Belém, 66075-110, Brazil.

Authors for correspondence*: [email protected] and [email protected] / LASEFI/DEA/FEA (School of Food Engineering)/UNICAMP (University of Campinas) Cidade Universitária “Zeferino Vaz”, Rua Monteiro Lobato, 80, Campinas 13083-862, Brazil. Phone: 055.19.3521.0100

Graphical abstract

Highlights 

An alternative algorithm for SFE kinetic modeling was validated



A SFE based Assaí processing waste reuse concept was developed



SFE is economically viable for bioactives obtaining from Assaí waste

Abstract Supercritical fluid extraction has been employed as a green alternative for the obtaining of extracts provided bioactive constituents from defatted Assaí waste. The effects of process parameters on the resulted overall extraction curves were evaluated using a spline model, which adjustable parameters used to analyze the extraction behavior were

the time span periods for the constant and falling extraction rates, i.e., tCER and tFER, and the extraction rate for the constant extraction period, MCER (or b1). The mathematical modeling was performed using an alternative algorithm implemented in MS Excel, which was able to provide reliable fittings comparable to SAS software. Although no report has been found to be compared to our economic evaluation findings, the costs of manufacturing (26.20-4.88 USD/kg extract) obtained in this work are competitive in the market, since the selling price of similar products worldwide is over 80.00 USD/kg of extract.

Keywords Assaí; Food wastes; Supercritical CO2; Mathematical modeling; Algorithm.

1 Introduction Assaí (Euterpe oleraceae Mart.) is mostly native to areas of Central and South America (Gale et al., 2014). In the state of Pará (Brazil), an important reloading point, the amount of berries produced by extraction was 124,421 tons, representing a value of approximately US$ 179 million (Gordon et al., 2012). The extracts obtained from crude Assaí berries contain phenolic acids such as vanillic, syringic, and ferulic (Pacheco-Palencia et al., 2009). Phenolic constituents are generally associated with health-promoting properties and the prevention of cardiovascular (Quiñones et al., 2013) and neurodegenerative (Schaffer et al., 2012) diseases. Assaí can be used as a healthier substitute for synthetic pigments, similarly as red cabbage (Xu et al., 2010), Andes berry

(Cerón et al., 2012), jabuticaba (Santos et al., 2013) and blackberry (Reátegui et al., 2014). The recovery of phytochemicals from solid wastes has been reported using conventional and alternative technologies. Conventional methods are Soxhlet extraction, maceration and vapor distillation (Ghitescu et al., 2015), requires long time processing and great expenses with solvent. Nevertheless, alternative methods of extraction with the use of non-toxic solvents like Supercritical Fluid Extraction, SFE (Paes et al., 2014), and pressurized liquid extraction (Santos et al., 2013; Santana et al., 2017) are promising approach of the integral use of plant wastes for the obtaining of high-quality extracts. The evaluation of overall extraction curves (OECs) using kinetic models is relevant for determining parameters used for scale-up, process design, and ensuring technical and economic viability of SFE processes at industrial scale (Fiori et al., 2014). In this context, the valorization of deffated Assaí waste (DAW) was investigated on the obtaining of bioactives extracts using SFE process, in terms of kinetic and economic perspectives. The OECs were studied using the spline, from which mathematical modeling was performed using SAS software and an alternative algorithm implemented in MS Excel. Process simulation and economic evaluation were performed using the Aspen Plus® v 8.4 and MATLAB (MATrix LABoratory) softwares.

2 Materials and methods 2.1 Raw material preparation Defatted Assaí waste (DAW) was obtained from cold-mechanical extraction technique applied previously to crude Assaí berries for the obtaining of oil. This waste was donated by Beraca/Brasmazon (Ananindeua, Pará, Brazil). The raw material was comminuted in a knife mill (TECNAL, TE 631/1, São Paulo, Brazil). The grounded raw material was classified according to the particle size using a vibratory system (BERTEL, 0701, São Paulo, Brazil) and then stored in a domestic freezer (ELETROLUX, H210 Skin, São Paulo, Brazil). 2.2 Experimental procedures Supercritical fluid extraction (SFE) was performed using a plant, donated by Technische Universität Hamburg-Harburg (TUHH, Germany), as shown in Fig. 1. The equipment consists of two thermostatic baths (model N3, Haake Mess-Technik GmbH, Karlsruhe, Germany), one for cooling and another for heating, a high performance liquid chromatography (HPLC) pump (Thermoseparation Products, Constametric model 3200 P/F, USA), control and micrometering valves. The mass flow rate of carbon dioxide was measured using a flow totalizer (Bopp and Reuter Mess-Technik GmbH, Maisach, Germany). Carbon dioxide (99.9% pure, Gás-Pará S.A., Belém, Brazil) was brought to the required pressure and then circulated through a fixed bed of solid (raw material) with 2.2 cm of diameter and the height of 33 cm. The mixture solute/solvent was expanded in the separator vessel and the extracts

were collected in test tubes. The expanded carbon dioxide passes through a gas volume meter and then is expelled to the atmosphere. The extractor pressure was measured using a Bourdon gauge (Wika, DIN.S, 0-40 MPa, 1 MPa) and its temperature by a thermocouple NiCr / Ni. Overall Extraction Curves (OECs) were determined using 50 g of grounded DAW (feeding mass) and constant solvent mass flow (QCO2) of 13.33 g/min. Two levels of temperature (313 and 323 K) and three levels of pressure (20, 25 and 30 MPa) were used. The extraction length was of approximately 200 minutes. Extraction yield, expressed as wt.%, was calculated by the ratio between the total mass of extract (g) and the feeding mass (g), in dry basis.

2.3 Mathematical modeling 2.3.1 Data fitting using SAS The spline fit was done using the procedures PROC REG and PROC NLIN of SAS 9.2 (Freund and Little, 1995). The REG procedure is a general-purpose procedure for regression and the NLIN procedure produces least squares or weighted least squares estimates of the parameters of a nonlinear model. 2.3.2 Data fitting using MS Excel An alternative algorithm implemented MS Excel (Santos, 2007; Santana, 2013; Santana, 2016) is proposed in this work. The validation was performed using the OECs of ginger at 303 K and 10 MPa (Rodrigues, 2001) and vetiver, obtained at 313 K and 20 MPa (Martínez, 2005). Comparisons with the SAS software (SAS Institute Inc., Cary, NC, USA) were established. An one-way Analysis of Variance and Tukey test were also

performed with a confidence range of ≥ 95%. The procedure PROJ.LIN associated to Visual Basic for Applications from MS Excel determines the interception point of the lines for the spline model. The flow chart of the process used for MS Excel on determining SFE kinetic parameters provided by Fig. 2. To use this algorithm is necessary to provide experimental information of solvent flow, feeding mass, and the time intervals on product collecting and the accumulated mass of extract in each time interval. The equations used for the modeling using the spline (Eqs. 1 and 2) consider the influence of the feed mass and solvent flow on the yield in extract, as previously established by Santos (2007) and validated for various extraction kinetics (Santana, 2013; Santana, 2016). Considering two and three-straight lines (Eqs. 1 and 2, respectively) this algorithm acts in an exhaustive search procedure (between zero time until the last time of extraction) for calculating the best values of period of constant extraction rate (tCER) and falling extraction rate (tFER), obeying the criterion to find the best fitting.

  Q m EXT  F0 b0  CO2  (b1t  b2 AL1 ) F0  

(1)

  Q m EXT  F0 b0  CO2  (b1t  b2 AL1  b3 AL 2 ) F0  

(2)

Where: b0 = linear coefficient (g); b1 = Slope coefficient (zero-order term, in g/min) of constant extraction rate (CER)

straight line, physically expressed as MCER, which is the extraction rate for the CER period; b2 = Slope coefficient (first-order term, in g/min) of falling extraction rate (FER) straight line; b3 = Slope coefficient (first-order term, in g/min) of diffusion controlled (DC) straight line; mEXT= Mass of extract (g) t= Time of extraction (min) AL1=maximum (t-tCER) and AL2=maximum (t-tFER) are dimensionless parameters associated to the exhaustive search procedure from the algorithm.

From the spline model using SAS and MS Excel the following parameters considering the CER period were estimated: YCER (g extract/g CO2), which is the mass ratio of extract in the supercritical phase at the bed outlet, which was obtained by dividing MCER by the solvent flow, and RCER (wt.%, g extract/g feed), which was obtained by the ratio between the accumulated mass of extract collected in the time span of the CER period (tCER) by the mass of feed. 2.4 Economic evaluation of the supercritical fluid extraction A flowsheet model of the DAW valorization was developed using the commercial software Aspen Plus® v 8.4 and the process integration and the thermo-economic evaluation was carried out using MATLAB (MATrix LABoratory) platform (Osmose, 2013).

The

thermodynamic

model

used

to

represent

the

process

was

Bene-

dict-Webb-Rubin-Starling (BWRS) model when supercritical fluid extraction was considered and UNIQUAC model for low pressure processes (Witkowski and Majkut, 2015). In this study the problem resolution was carried out following the steps: 1. Process data is gathered from experimental results; 2. Aspen Plus® flowsheeting software was used to model mass and energy flows of the process. The model was used to calculate the associated heat and power balances; 3. Pinch analysis methodology (Linnhoff, 1982) was used to perform the thermal integration of the process aiming at the reduction of process steam requirements; 4. An economic model was developed using data obtained from the flowsheeting software Aspen Plus® and the results obtained by the thermal integration model. The proposed SFE configurations were evaluated regarding productivity and economic process indicators. 2.4.1 Aspen Plus® mass and energy simulation Aspen Plus® flow sheeting software was used to simulate mass and energy flows of the process. The simulation was used to calculate the associated heat and power balances. The SFE process was simulated considering that the raw material, DAW, was initially milled and sent to the extraction vessel. CO2 is cooled to 247 K and compressed to the desired pressure. It is then heated to the extraction temperature, reaching the supercritical conditions. Later, the extraction vessel of 1 m3 is packed with the raw material

and the supercritical fluid is passed through it. As the process was studied in a stationary regimen, it was considered 2 SFE unities working in parallel to achieve a continuous inlet and outlet material flow. After the extraction process, the extract diluted in supercritical CO2 is sent to a depressurization tank to separation. At this stage, the pressure is reduced to 5 MPa and temperature is set at 298 K, gasifying the carbon dioxide and separating it to be recycled to the process. It was considered a 3% of CO2 loss. The SFE process was evaluated considering the two levels of temperature (313 and 323 K) and three levels of pressure (20, 25 and 30 MPa) used experimentally. S/F (Solvent mass to Feed mass ratio) was calculated considering the obtained tCER data obtained by the for MS Excel model. Scale-up of the process can affect mass transfer and therefore the behavior of the process and its yields. Comparing a process in laboratory and pilot scale, in some cases the extraction yields increases due to different factors such as the co-extraction of water, the higher solvent superficial velocity causing mechanical dragging, and the higher efficiency of the separators in recovering the extract at larger scale (Prado et al., 2011). As a preliminary analysis, an accepted approximation to obtain yields for industrial scale, is to assume that for a given process time, the extraction behavior has the same performance as that obtained experimentally in the laboratory scale unit when the solvent to feed mass ratio and operating parameters (temperature, pressure, density and porosity) are kept constant (Veggi et al., 2014). It is known that the introduction of extracts with high antioxidant capacity into food and/or medical fields is a technological challenge since these compounds have low sta-

bility to the environmental conditions during processing and storage (Cavalcanti et al., 2011; Santos et al., 2013). Extract encapsulation and/or other techniques could be used to enlarge storage time of the obtained extract. Even though, no post-processing technique was assumed at this stage of the analysis. 2.4.2 MATLAB thermal process integration simulation Thermal process integration and recovery are important regarding the process performance because several parts of the process operate at high temperature. Initially, data from the Aspen Plus® simulation is recovered and energy integration is performed on the basis of the pinch analysis methodology (Linnhoff, 1982). For this analysis the data for CO2 initial cooling, heating and recirculation were used. The thermal effects of each sequence of operations are grouped and constitute the units whose flow rates are to be computed in the integration problem. All heat streams generated by the energy and mass flow model were considered in the thermal integration analysis. 2.4.3 MATLAB simulation-based economic evaluation The economic evaluation was built in MATLAB. The economic evaluation done here mainly consists in the determination of the total investment cost and cost of manufacturing (COM) for each SFE process condition. The major process units are sized by extracting information of the Aspen Plus® simulation. In order to accomplish an economic evaluation of the process viability at industrial scale, lab results were scaled-up considering that the same performance would be obtained. This criterion, which has been used by other authors for SFE process (Pereira

and Meireles, 2010), assumes that the process will have identical performance with respect to yield at the laboratory and industrial scales if the same process conditions are used (temperature, pressure, extraction time, etc.). 2.4.4 Process performance indicators The SFE process was analyzed based on productivity and economic performance indicators. The indicators were chosen in order to identify which process parameters adopted for the SFE lead to the higher extract production at the lower investment and cost of manufacturing. Although many different process indicators could be assumed to perform this comparison, the indicators adopted represented a preliminary approach to envision process bottlenecks in order to improve future studies and easy decision making. Productivity performance indicators: The productivity performance indicator was calculated based on the mass balance results of the Aspen Plus® software. It was evaluated the process productivity per year. Economic performance indicators: To evaluate the process in terms of economic parameters, the performance indicators assumed were the total investment cost and cost of manufacturing (COM). In the simulation, the cost the major process equipment are roughly sized and their purchase cost is calculated and adjusted to account for specific process pressures and materials using correlations from literature (Turton et al., 2009; Ulrich and Vasudevan, 2003). Equipment costs are updated using the annual Chemical Engineering Plant Cost Index (CEPCI) for 2016. The total investment is then calculated

using multiplication factors to take into account indirect expenses like installation costs, contingencies and auxiliary facilities. COM estimation for each SFE process condition was calculated based on the methodology of Turton et al. (2009) as presented in Eq. 3 COM = (VC + FC + GE)*(1 + 0.03COM + 0.11COM + 0.05COM)

(3)

In which, 0.03COM represents the royalties; 0.11COM the distribution and selling and 0.05COM the research and development investments. VC is the variable cost, representing the operational costs which are dependent on the production rate and consist in raw material costs, operational labor, utilities, among others. FC is the fixed cost, representing the costs that do not dependent on production rate and include territorial taxes, insurance, depreciation, etc. GE is the general expenses, it cover business maintenance and include management, administrative sales, research and development costs. These three parameters are calculated in terms of five main costs: total investment cost, cost of raw materials, cost of utilities, cost of operational labor and cost of waste treatment. The raw material cost was calculated based on the DAW cost and cost for CO2 reposition, as part of the solvent is lost during the process. Utility costs considered the electricity and heating and cooling requirements. The cost of waste treatment was neglected because the solid generated in the extraction process can be used in different segments as energy production (Prado et al., 2014) or even as nutritional source for several agricultural sectors (Odlare et al., 2011). Cost of operational labor was calculated based on the methodology of Turton et al. (2009). To enable a better visualization of each alternative evaluated the COM per mass of

extract (COMextract) was calculated dividing the COM per the dry mass of extract obtained. Table 1 shows the list of assumptions that support the economic assessment results. 2.4.5 Sensitivity analysis The initial economic evaluation considered two SFE extractors of 1m3 with the inlet flow of 400 kg/h of DAW. The state of Pará is the main Brazilian region in production and consumption of Assaí, representing around 93% of the national production. The Assaí consumption in the city of Belém, capital of Pará region, generates 350 tons per day of waste material (seeds and bagasse) (Silveira, 2012). Considering that 85% of the waste mass is comprised of seeds (Gabriel, 2012), a total mass flow of 2.2 t of DAW/h is available to be processed. A sensitivity analysis was accomplished in order to evaluate the influence of increasing the mass flow of DAW to the process on the economic performance indicators. It was evaluated the values of 18, 30, 60, 80 and 100% of the total DAW mass flow available at the city of Belém (2.2 t/h). 3 Results and discussion 3.1 Experimental procedure Characteristics of process, such as extraction yield, solubility and selectivity are the function of pressure and temperature (Felföldi-Gáva et al., 2012). The extraction yields obtained with supercritical CO2 at different operational conditions (Table 2) consists of the maximum amount of the solute that is extractable from a botanic matrix. This property is useful for scale-up studies.

To the best of our knowledge there is no study on the use of the DAW obtained by cold-mechanical extraction technique for obtaining bioactive compounds. On the other hand, using the entire Assaí fruit Rufino and co-workers (2011) and Silva and Rogez (2013) obtained extraction yields of 20.82 wt.% and 50 wt.%, respectively, using petroleum ether as extracting solvent in a Soxhlet apparatus. Ayala (2012), also using crude Assaí berries as raw material, obtained extraction yields of 11-46.8 wt.% by SFE with CO2, at 11.3-60 MPa, indicating that supercritical CO2 is a good solvent for bioactive compounds recovery from crude Assaí, resulting on extracts with high quality, when compared to those obtained by petroleum ether, a toxic solvent. The SFE process applied to DAW resulted on extract yields varying between 5.5-7.6 wt.% (Table 3). The raw material used in this work still had a fraction of extract that was inaccessible to the previous process of cold-mechanical extraction. High solvent flow rates associated with the temperature and pressure conditions used in this work favored the availability of the material of interest to be carried by supercritical CO2. Therefore, we can conclude that the solids leftover after cold-mechanical extraction still contain significant amount of bioactive compounds that is not valorized in the current industrial process design. 3.2. Mathematical modeling The process and calculated kinetic parameters for ginger and vetiver OECs are available in Table 2. The OECs adjusted by the spline model calculated by the algorithm implemented in MS Excel described experimental points similarly to those calculated by

SAS for vetiver (Fig 3.A, 3 lines) ginger (Fig 3.B, 2 lines). Mean square error (MSE), standard deviation (SD) and correlation factor (R2) of measurements calculated for the MS Excel modeling were close to those obtained by SAS for most of kinetics, with deviations lower than 10%, in accordance to those reported in literature (Cabeza et al., 2016). The kinetic behavior of vetiver is a typical three-straight lines OECs (Fig 3.A). This kind of behavior is indigenous from raw materials which initial concentration of solute is too low or not available to the solvent. The kinetic behavior of ginger is described by two-straight lines (Fig. 3B), which is characteristic from raw materials that suffered previous processing that increased the availability of compounds of interest to be carried out by the solvent. In general, the OEC kinetics obtained in this work (Figs 4 and 5) presented an intermediate behavior between 2 and 3 straight-lines, characterized by 3 periods: a first step controlled by external diffusion, a second stage controlled by external and internal diffusion, and the third step controlled by internal diffusion. Different raw materials require a particularity for modeling their experimental kinetic extraction curves. To do so one can use the spline model due to this method provides flexibility to adjust two or three lines, according to the individuality of each raw material. Initial estimates are necessary to calculate the values of the adjustable parameters in SAS software (SAS Institute Inc., Cary, NC, USA). Depending on the given estimatives, the parameters generated by the program may represent, or not, the reality of the studied phenomenon. Nevertheless, for the algorithm implemented MS Excel this

step is not necessary because an exhaustive search procedure inserted in the VBA because of the calculated optimized values of tCER and tFER attributed to the exhaustive research procedure. Table 3 presents the adjustable parameters of DAW OECs and statistical evaluation provided by standard deviation (SD), mean square errors (MSE) and correlation factor (R2). The behavior of the OECs obtained at 313 K is quite similar (Fig. 4), while those obtained at 323 K differed presented slight variations for the studied pressures (Fig. 5). In the studied kinetics, the CER and FER steps evidenced by their respective time-span periods, varied approximately between 25-50 minutes and 50-100 minutes (Table 3, and Figs. 4 and 5) The increase in temperature at lower 20 MPa increased the yield in extract and the YCER. Nevertheless, at 25 MPa the rising temperature decreased these attributes (Table 3) similarly to those reported to SFE of rice bran waste (Jesus et al., 2013). On the other hand, at 30 MPa, the increase in process temperature increased extract yield. This anomaly comes about because density decreases dramatically with an increase in temperature at low pressure, whereas at higher pressure, changes in temperature have much less effect on density. Therefore density, not pressure, to a first approximation is proportional to the solvent power of the solvent (Otles, 2017). The increase in pressure increased MCER, RCER values at 313 and 323 K, probably for the same reason.

For many industrial applications, the extraction process ends shortly after CER period because the best operational conditions will be those in which a high amount of extract (50-90 wt.%) is obtained in a relatively short process time, justifying why it is usual and important to determine some kinetic parameters which characterize the CER region of the OECs (Meireles, 2008). The RCER value represented between 40-80 wt.% of the total extraction (5.5-7.6 wt.%) obtained at the end of the OEC (Table 3), corroborating with the range of 50-90 wt.% established previously for the CER period. A spline curve is a mathematical representation for which it is east to build an interface that will allow a user to design and control the shape of complex curves and surfaces In fact, this model is purely empirical and does not take into account the phenomenological matters, i.e., interactions between the solute and the solid matrix or the fractioning of the solute during the extraction process (Judd, 1998). Despite its empirical nature, the spline model could be related to the mass transfer phenomena, associated to the broken and intact cell model (Sovová, 1994) by several authors (Albuquerque and Meireles, 2012; Jesus et al., 2013; Povh et al., 2001) since the experimental and modeled curves showed the distinctive three regions: CER, FER, and DC, highlighted by the time-span periods of CER and FER regions, i.e., tCER and tFER. Therefore, SFE curve modeling supplies information regarding the dominant mass transfer mechanism on supercritical extraction, assisting the definition of methodology for scale-up and economic evaluation studies (Benelli et al., 2010; Pereira and Meireles, 2010).

3.3 Economic evaluation The results for the raw material consumption and productivity parameters are presented in Table 4. In all evaluated alternatives the same amount of DAW is used as raw material to the extraction. It is possible to note that the amount of CO2 strongly influences the electricity consumption and cold demand under 293 K of the SFE process as well as the adopted pressure. Fig. 6 shows the total investment cost necessary for the evaluated alternatives. The total investment cost calculated for the evaluated alternatives was around 3.63 MUSD. The highest investment cost, 5.8 % higher than the medium value, is found when SFE is conducted at 313 K and 30 MPa. In this alternative, not only the cost is increased by the increase of pressure but also due to the high tCER calculated resulting in a high S/F (Solvent mass to Feed mass ratio) The total investment cost decreased with the increase of temperature due to the lower S/F necessary at this configuration (Table 5), what lead to a lower investment on the CO2 related equipments (CO2 pump and flash tank after SFE). The cost of manufacturing (COM) calculated for the evaluated configurations was similar for all evaluated configurations (Table 5). The main contribution for the COM was the variable cost, around 46 % of the COM. Usually, the raw material contributes with around 90 % of the variable cost for the extract production from SFE (Pereira and Meireles, 2010). Here, since the raw material is an industrial by-product, its cost (0.02 USD/kg, value estimated based on the Assaí price, Gabriel, 2012) is lower than a medicinal plant, growth for providing a bioactive compounds-rich extract. Indeed, the raw

material contribution in the variable cost in this case was only 46 %, being the Assaí waste contribution 13 % and the CO2 reposition contribution 59 %, demonstrating that the reduction on CO2 losses and/or price would lead to a significant reduction of COM, being an important bottleneck for the SFE from DAW. The COM calculated in terms of extract (COMextract) shows the mean value for extract production of 19.36 USD/kg of extract. Lower COMextract are found when higher pressure are applied, and due to lower S/F and higher YCER are found when higher temperature is employed, the lowest COMextract is found for Experiment data 6, 323 K/30 MPa (Table 5). A number of studies involving natural products have shown the viability of SFE through cost-of-manufacturing analysis. In the majority of these studies, the manufacturing costs of extracts produced by SFE were smaller when compared to the selling price of the extracts (Pereira and Meireles, 2010). In the case of Assaí extract from cold-mechanical Assaí processing residue, we also observed this fact since its selling price would be the same as that from cold-mechanical Assaí berries (80-120 USD/kg of extract (depending on the amount purchased) (Alibaba, 2017). This is a very interesting result since the proposed use here of the DAW would be beneficial for the entire Assaí berry valorization process as increased its overall economic attractiveness, producing higher amount of product not increasing the plantation of Assaí. Analyzing the impact of increasing the DAW mass flow to the SFE process (Fig. 7) it is possible to decrease COMextract to 4.88 USD/kg of extract when the total mass flow of Assaí waste from Belém city is considered, improving even more the economic at-

tractiveness of our proposed alternative use for this solid residue, which is nowadays as mostly food industries residues used as animal feed and/or for agricultural purposes (Santos et al., 2014). As it is known, increasing the size of SFE unit increases the investment cost but decreases the production cost per mass of extract produced (Viganó et al, 2017).

4. Conclusions The kinetic study of SFE from Assaí processing residue as function of the temperature and pressure variables indicates that these parameters affected the process mass transfer rate, the CER period and the extraction yield. Extraction yields were in the range 5.5-7.6 wt.%. As expected, the extraction yields were lower comparing to the ones obtained by other researchers, as the raw material used in the present study is a process waste of cold-mechanical extraction. Supercritical extraction process demonstrated that could effectively recover the fraction of extract that was inaccessible to the previous extraction process. The best operational conditions for SFE concerning the yield, 313 K/25 MPa and 323 K/30 MPa, favored the availability of the material of interest to be carried by supercritical CO2. The validation of an alternative algorithm implemented in MS Excel was also investigated. Because of simplicity of the spline methodology, it can be easily programmed in the preferably software, providing results according to those obtained by SAS software. MS Excel, an easily accessible computational tool, showed similar relia-

bility on describing the Overall Extraction Curves (OECs) for defatted assaí waste. Regarding the economic evaluation of the use SFE process aiming at the Assaí processing residue valorization, we demonstrated that it is a promising approach since the estimated manufacturing costs were smaller when compared to the selling price of the extracts.

Acknowledgements Ádina L. Santana thanks to CNPq (Process 133858/2011-1) and CAPES for her Masters and PhD financial fellowship, respectively. Beraca/Brasmazon is also acknowledged for supplying defatted Assaí waste. Juliana Q. Albarelli thanks FAPESP (processes 2013/18114-2; 2015/06954-1) for the post-doctoral fellowships. Diego T. Santos thanks CAPES (process 7545-15-0) for the post-doctoral fellowship. M. Angela A. Meireles thanks CNPq for the productivity grant (302423/2015-0). The authors acknowledge the financial support from CNPq (process 486780/2012-0) and FAPESP (processes 2012/10685-8; 2015/13299-0).

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Figure Captions Fig. 1 - Schematic diagram of raw material and the SFE unit used in this work.

Fig. 2 - Flow chart of the process used for MS Excel on determining SFE kinetic parameters using two (A) and three (B) straight lines.

Fig. 3 - Modeled OECs of vetiver (A) and ginger (B) used for the validation of MS Excel algorithm.

Fig. 4 - Experimental OECs obtained at 313 K in the pressures of 20 MPa (A), 25 MPa (B) and 30 MPa (C) for DAW.

Fig. 5 - Experimental Assaí OECs obtained at 323 K in the pressures of 20 MPa (A), 25 MPa (B) and 30 MPa (C) for DAW.

Fig. 6 - Total investment cost for each configuration evaluated.

Fig. 7 - Effect of increasing the DAW mass flow on the total investment and COMs.

Table Captions Table 1 - List of assumptions of the economic analysis. Economic data

Value

Unit

Days worked in a year

320

(days/year)

DAW

0.021

(USD/kg)

CO2

0.30 2

(USD/kg)

Electricity

0.05 3

(USD/kWh)

Cold demand under 293K

0.028 4

(USD/kWh)

Raw materials prices

1

value estimated based on the assaí price (Gabriel, 2012); 2data from (Santos et al, 2014); 3

data from (Albarelli et al, 2014); 4data from (Pereira and Meireles, 2010)

Table 2 – Process and kinetic parameters for vetiver and ginger OECs used for the validation of MS Excel algorithm. Process parameters Feeding mass (g)

X0 (%)

QCO2 (g/min)

Vetiver

30

0.45

0.85

Ginger

80

0.97

1.30

Spline model parameters. Vetiver SAS

Ginger

Excel -4

SD* -5

SAS

Excel

SD*

-3

b0 (g/min)

-9.20×10

-3.14×10

0.87

-0.13

-2×10

0.09

b1=MCER(g/min)

4.35×10-3

5.12×10-3

0.54

3×10-3

2×10-3

0.71

b2(g/min)

-3.44×10-3

-3.40×10-3

0.42

-9.80×10-4

-7×10-4

1.98

b3(g/min)

-4

-7.10×10

-8.70×10

-4

1.13

-

-

-

tCER (min)

20.23

20

0.16

192

192

0

tFER (min)

51.71

0.50

-

51 -3

YCER (g extract/g CO2) 5.12×10 RCER (%, g extract/g feed) MSE R2

0.287

6.02×10

-3

0.286

0.06 8.35×10-4

2.23×10

-3

1.67×10

-3

8.83×10-4 0.503

0.504

7.05×10-7

7.28×10-7

1.08×10-4

4.02×10-5

0.99

0.99

0.99

0.99

ρCO2 (g/ mL)**

*SD – Standard deviation between the results obtained with SAS and Excel.

Solvent density (ρCO2) Temperature (K)

Pressure (MPa)

ρCO2 (g/ mL)**

Temperature (K)

Pressure (MPa)

313

20

0.84

323

20

0.79

25

0.88

25

0.83

30

0.91

30

0.87

**Data from NIST webbook

0.04

Table 3 - Kinetic parameters of DAW OECs. 313K 20 MPa 6.0±0.2

Global yield (%) SAS

8.10×10-6

×10-3 4.16

b1=MCER (g/min)

×10-2

b2 (g/min)

-0.03

-2 ×10-3 -6.9×10

-4

6.4±0.2

SD*

SAS

EXCEL

SD*

SAS

EXCEL

SD*

1×10-3

-9×10-3

7.99×10-4

0.01

0.36

5.73×10-3

0.25

0.03

0.07

4.76×10-3

0.04

0.05

4.29×10-3

0.03

0.02

-0.03

-3.38×10-3

0.02

-0.04

-3.01×10-3

0.03

0.01

-0.02

-9.75×10

-4

3.21×10-3

-3

30 MPa

7.6±0.1

EXCEL

1.85 b0 (g/min)

25 MPa

b3 (g/min)

-9.1×10

tCER (min)

52.2

52

0.14

26.44

tFER (min)

108.6

108

0.42

66.44

-3

-1.06×10

-3

6×10-3

0.02

-9.7×10

25

1.02

41.54

42

0.33

68

1.11

88.60

90

0.99

YCER (g extract/g CO2)

3.12×10-3 2.35×10-4

0.20

4.97×10-3 3.26×10-4

0.32

3.98×10-3 3.22×10-4

0.26

RCER (%, g extract/g feed) MSE R2

4.56 6×10

4×10-3

4.55

-3

0.99

6×10

-3

0.99

3.71

3.48

0.01 0.99

0.16

5.19

4.91

0.01

0.01

0.10

0.99

0.99

0.99

0.20

323K 20 MPa 6.5±0.2

Global yield (%) b0 (g/min)

b1=MCER (g/min) b2 (g/min)

25 MPa

30 MPa

5.5±0.3

7.2±0.2

SAS

EXCEL

SD*

SAS

EXCEL

SD*

SAS

EXCEL

SD*

0.04

1.05×10-3

0.03

-0.36

-5×10-3

0.25

0.47

9.10×10-3

0.33

0.04

-3

0.09

-3

0.06

-4.40×10

-3

0.03

-3

0.03

2.94×10

-0.02 -3

-1.27×10

-3

-1.09×10

-3

0.03 0.02

0.05

3.75×10

-3

-0.03

-2.73×10

0.01

-0.01

-7×10

-4

b3 (g/min)

-9×10

tCER (min)

40

35

3.54

32.72

tFER (min)

67.74

64

2.65

100.50

2.52×10-3

1.9×10-4

0.16

3.52

3.53

8×10-3

-3

0.03 0.02

6.80×10

-0.05

0.01

-0.04

-1.97×10

27

4.03

19.87

21

0.80

107

4.60

42.42

49

4.65

0.21

5.98×10-3

4.5×10-4

0.39

0.35

3.73

3.69

0.02

YCER (g extract/g CO2)

3.73×10-3 2.81×10-4

RCER (%, g extract/g feed) MSE 2

R

-3

0.01

8×10

0.99

0.99

2.26 8×10

-3

0.99

2.77 0.134

8.9×10

0.99

0.99

-3

0.17 0.99

*SD - Standard deviation between results determined using SAS and MS Excel.

Table 4 - Raw material consumption and productivity parameters Experimental

1

2

3

4

5

6

Temperature

313

313

313

323

323

323

K

Pressure

20

25

30

20

25

30

MPa

13.9

10.1

11.2

9.3

7.2

8.0

4.20

4.2

5.0

2.8

2.0

5.7

data

S/F (Solvent mass to Feed mass ratio) YCER

(g extract/gCO2)

Raw materials consumption DAW

3,072

3,072

3,072

3,072

3,072

CO2

1,281

931

1,032

857

857

Electricity

1,521

1,591

1,771

1,348

1,529

1,503 MW

2,934

2,132

2,364

1,963

1,963

1,689

Cold demand un-

3,072 t/year 737

t/year

MW

der 293K Productivity performance indicators SFE extract

118.7

118.7

141.3

109.8

87.2

193.5 t/year

Solid residue

2,708

2,708

2,685

2,747

2,770

2,665 t/year

Table 5 - Cost of manufacturing (COM) for the evaluated process Experimental data

1

2

3

4

Temperature

313

313

313

323

323

323

K

Pressure

20

25

30

20

25

30

MPa

13.9

10.1

11.2

9.3

7.2

8.0

4.20

4.2

5.0

2.8

2.0

5.7

S/F (Solvent mass to

5

6

Feed mass ratio) YCER

g extract/ g CO2

Economic performance indicators Variable cost

1.21

1.08

1.14

1.03

1.05

1.01

MUSD/year

Fixed cost

0.82

0.80

0.83

0.76

0.79

0.81

MUSD/year

0.08

0.08

0.08

0.08

0.08

0.08

MUSD/year

COM

2.51

2.33

2.45

2.22

2.29

2.26

MUSD/year

COMextract

21.17

19.61

17.33

20.18

26.20

11.69

USD/kg extract

General production cost