A FRAMEWORK FOR SOLVENT SELECTION BASED ON HERBAL EXTRACTION PROCESS DESIGN

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1 Journal of Engineering Science and Technology Special Issue on SOMCHE 2014 & RSCE 2014 Conference, January (2015) School of Engineering, Taylor s University A FRAMEWORK FOR SOLVENT SELECTION BASED ON HERBAL EXTRACTION PROCESS DESIGN S. N. H. M. AZMIN 1, N. A. YUNUS 1, A. A. MUSTAFFA 1, S. R. WAN ALWI 1, *, L. S. CHUA 2 1 Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM, Johor Bahru, Malaysia 2 Institute of Bioproduct Development (IBD), Universiti Teknologi Malaysia, UTM, Johor Bahru, Malaysia *Corresponding Author: shasha@cheme.utm.my Abstract In the extraction of Malaysian herbs phytochemical, the uses of solvents are very important as a transfer medium. Most of the current studies are only focusing on the effect of using different solvent types, different solvent to herbs ratio, effect of phytochemicals to the scavenging activity, antioxidant property and so on. There are very limited literatures on solvent blended design methods for phytochemicals extraction from herbs. Practically, different solvent can only extract certain phytochemicals that have the same number of partition coefficient. In this study, both solvents and phytochemicals properties are concerned in order to design a blended solvent that can enhance the extraction process and extract the optimum amount of phytochemicals from herbs. In addition, the safety, economic and environmental aspects of the solvent are also considered. The main objective of this work is to design solvent blends for the extraction of herbal phytochemicals using computer-aided approach. The methodology is divided into four tasks which are Task (I): Problem definition, Task (II): Property model identification, Task (III): Design solvent blend and Task (IV): Model based verification. In this paper, only Task (I) up to Task (III) is illustrated for calculation. Task (IV) will be presented in future paper. This proposed method has been applied to design a solvent mixture for the extraction of Kaempferol from Kacip Fatimah herb as a case study. From the analysis, 17 feasible binary solvents mixture have been identified suitable for the extraction as it was within range of the design target. Keywords: Extraction, Product design, Solvent blend, Phytochemicals, Kacip Fatimah. 25

2 26 S. N. H. Mohammad Azmin et al. Nomenclatures ζ k Targets properties ζ LB Target values lower bound ζ UB Target values upper bound k ζ S Solvent target value k,m ζ i Pure solvent property k of compound i in the mixture m x i mole fraction of compound i,, Lower bound composition for a binary mixture, m,, Upper bound composition for a binary mixture, m Log K ow Partition coefficient T b Boiling point log LC 50 Toxicity parameter Greek Symbols µ Viscosity ρ Density δ Solubility parameter Activity coefficient H fus, Heat of fusion Melting point T mi Abbreviations PI 1. Introduction Performance Index Extractions of Malaysian herbs have been widely done by other researchers. However, from the literatures, it was found that limited study has been done on the relationship between the solvent properties to the phytochemicals properties. Most of the researchers only focus on the extraction yield by using different solvent, effect of solvent-malaysian herbs ratio to the extraction yield, effect of phytochemicals to the scavenging activity, antioxidant property and so on. As an example, Karimi, Jaafar [1] investigated the total extraction yield in Labisia Pumila (Kacip Fatimah) and its antimicrobial activities. Filly, Fernandez [2] combined two methods (microwave heating and distillation) to increase the extraction yield which is the essential oil of Rosmarinus Officinalis L. (Rosemary). Meanwhile, Konar, Dalabasmaz [3] determined the caffeic acid derivatives of Echinacea purpurea aerial parts under various supercritical fluid extraction (SFE) conditions. The main issue in solvent selection used in herbal extraction is the decisions are based on trial-and-error method. Traditional methods mainly focused on experimental analysis by clasifying solvent (polarity) for different solute [4]. This method is however time and resource intensive [5]. In addition, to extract one phytochemical, at least six solvents are needed. The reduction of solvents needed can reduce the cost and time, as well as increase or maintain productivity [6]. The combination of property predictive models with computer-assisted search [5] is one way to reduce experiments needed which to be conducted.

3 A Framework for Solvent Selection Based on Herbal Extraction Process Design 27 From the literature review, no reported publication has been found on a framework for selecting the suitable solvent blend that can extract the optimum phytochemicals from Malaysian herbs by using computer-aided approach. Some relevant literature such as Karunanithi et al. [1] designed an optimal extractant/solvent for the separation of acetic acid from water by using Liquid- Liquid Extraction (LLE), Conte et al. [2] and Conte et al. [3] designed a solvent blend for paint and insect repellent formulation and Yunus et al. [4] designed a framework for blending gasoline and lubricant base oils for production of green gasoline. Thus, the objective of this research is to develop a systematic and generic approach to design solvent blends that could lead to reduction of solvent waste, extraction time and cost, and increase the phytochemicals extraction yield production for herbs. 2. Methodology In product-process design reverse approach is used somewhere else [5], where there are two stages involved. The first stage is to define the design target while the second stage is to identify the alternatives that match the target. This approach was applied for the reason of all processes depends on some key properties of the product and on the effect of these properties on the process performance [6]. In the first stage, product performance target are set and relevant property models are identified. In the second stage, appropriate models are used to identify a list of products which matches the target properties. Figure 1 shows a systematic methodology for designing a solvent blend. As shown in Fig. 1, Task (I) and (II) follow the first stage and Task (III) and (IV) follow the second stage. Task (IV) is needed to prove that the listed solvent blends can be used in the selected process as it is verified with the experimental result. Fig. 1. Systematic Methodology for Designing Blended Solvent.

4 28 S. N. H. Mohammad Azmin et al Tasks of the methodology Task (I): Problem definition Task (I) is used to define the problems in matching the user needs. The goal for this task is to get the properties related to the selected process and to set the boundary for the selected property. In this task, the mechanism in the selected process will be considered to identify the requirements to improve the process and the factor that can increase the process efficiency. All requirements and factors will be listed in this study. The important needs that have been listed will then be translated into performance criteria. From these performance criteria, the properties related to the selected process will be considered in the study and listed down. In this step, constraints of the listed properties are also specified. All the constraints are set depending on the literature search and existing specified constraints for the selected process Task (II): Property model identification The goal for Task (II) is to find the suitable property model for the selected process. For this task, the property models from the literature are collected and tested on its suitability with the selected process. The pure target properties could be predicted from group contribution method while the mixture target properties could be predicted using the property models from literature if available. The most suitable model will be used to predict the properties of single and blended solvents Task (III): Design solvent blend The next task is to design the solvent blend. First, the database of the solvent candidates and phytochemicals must be selected. The database needed is the phytochemicals list with their properties, and a list of chemicals/solvents with their associated properties. The potential solvent blends that match all the properties constraints set in Task (II) are then listed. Through this task, the goal of finding the optimal solvent blend can be obtained Mixture design algorithm Mixture Design Algorithm is applied to design the solvent mixtures by matching the constraints of the listed properties. The outputs of this task will be a mixture that matches the targeted composition, cost, target property value and the solvent blend stability as shown in Fig (a) Level 1: Pure component constraints At this level, the pure component properties of solvent in the database and target phytochemicals are compared with respect to the target values. The aim for this level is to get the list of pure solvent that match the phytochemical target properties value. The targets properties, ζ k of the solvent in the database are compared with the target properties, ζ k of each phytochemicals and with the target values boundaries, ζ LB and ζ UB. The solvents are rejected if the property value of the solvent are either lower than the lower bound values (ζ S k <ζ LB k ) or greater than the upper bound values (ζ S k >ζ UB k ).

5 A Framework for Solvent Selection Based on Herbal Extraction Process Design (b) Level 2: Linear design constraints Binary mixture screening of solvent is starting at this level. Linear constraints are related to the properties described by linear models. In this case study, linear models are following the linear mixing rule to compute the mixture target property. For binary mixture, the generic form of the linear model is:, =, =.,, + 1. In this equation, subscript 1 and 2 indicates solvent 1 and 2 in the binary mixture; ζ i k,m is the pure solvent property k of compound i in the mixture m; x i is the mole fraction of compound i. In this step, the composition boundaries for each target properties of solvent in binary mixture are calculated using the equation 2., is a specific target value for property k., =,,,, (2) The composition range of solvent 1, (,, and,, ) for a binary mixture, m is calculated as follows based on eqs. (3) and (4):,, = (3),, = (4) The overall composition range (,, and,, ) for each mixture is set by comparing the composition range of all target properties. The minimum and maximum values of,, and,, are calculated by eqs. (5) and (6) for each property k used as follows:, =max,, (5), =max,, (6) The solvent mixture with the composition range of each property which does not overlap each other is rejected. (1) (c) Level 3:Non-linear design constraints At the end of level 2, binary mixtures candidates with their compositions boundary have been determined. In this third level, non-linear constraints are applied for further screening of the solvent mixtures. For this step, the non-linear mixture properties,, for the remaining binary mixtures and new composition ranges which satisfy the non-linear constraints are determined. The mixtures are rejected for which the calculated property values do not match the non-linear property constraints (d) Level 4: Phase stability constraints At the end of level 3, mixtures that do not satisfied the target properties are rejected. In this level, the mixture s stability is determined, where the unstable

6 30 S. N. H. Mohammad Azmin et al. mixtures will be rejected. Phase split should not occur between the binary mixture candidates. The result from this stability step is either the mixture (binary pairs) is totally miscible, partially miscible or immiscible. The mixtures showing phase split at the design composition are rejected (e) Level 5: Cost and extraction yield calculations This level is divided into two parts which are cost calculation (Level 5A) and extraction yield calculation (Level 5B). In level 5A, the binary mixtures with their composition are evaluated with the solvent price. Meanwhile in level 5B, the binary mixtures with their composition are also evaluated for phytochemical compositions in extraction yield. In this level, both cost and extraction yield can be calculated simultaneously. The results are then combined to get the optimum phytochemical yield with the lowest cost solvent used in extraction. The remaining binary solvent mixtures are ranked according to increasing cost and extraction yield Task (IV): Model-based verification The verification task is to ensure that the target properties of the candidate solvent mixtures estimated with rigorous models are satisfying the constraints. If the mixture properties are not in the target properties constraints, the listed solvent blend candidates will be rejected. In this paper, Task (IV) will not be presented. 3. Case Study: Solvent Blend for Extracting Kaempferol (Phytochemical) from Kacip Fatimah Herb The aim of this case study is to design a solvent blend that can maximise the extraction of Kaempferol, which is one of main phytochemical in Kacip Fatimah herb. The blend solvent formulation is considered for non-consumable phytochemicals product. The solvent blend that will be designed are considered to be used for the conventional extraction and the temperature considered is 90 o C as experimental run by Karimi et al (2011)[7]. 30 solvents data were used consisting of alcohol, hydrocarbon, ether and ester solvent categories. The main phytochemicals in Kacip Fatimah have been identified to be Kaempferol, Myricetin, Quercetin and Rutin. In this paper, the case study followed the systematic methodology in Fig. 1, from Task (I) to Task (III). In Task (III), the case study is only analysed from level 1 until level 3 (non-linear design constraints) Task (I): Problem definition Following are the main characteristics which have been identified from knowledge base for the selection of solvent blends for phytochemical extraction from herb: can effectively extract the selected phytochemicals from herb, can be removed from the solvent and crude extract mixture, have low toxicity, must be miscible to each other, low price and good solvent appearance. According to the knowledge base, the solvent desired characteristics needs to be translated into target properties. Referring to the existing solvent used in extraction process and consulting the knowledge base, the constraints corresponding to the target properties were defined as stated in Table 1.

7 A Framework for Solvent Selection Based on Herbal Extraction Process Design Task (II): Property model identification The target properties which are partition coefficient (log k ow ), toxicity parameter (LC 50 ), solubility parameter (δ), viscosity (µ), density (ρ) and cost (C) are estimated by using linear mixing rules while the others are predicted by using non-linear models. The linear mixing model is represented by Eq. (7). = (7) Table 1. Target Property Constraints for Herb Solvent Blends Design. Target property value Property Solvent constraints Phytochemical constraints Partition coefficient Log K ow (depends on phytochemicals) -0.3 Log K ow 4.44 Boiling point K T b K - Toxicity parameter log LC Viscosity 1.20 cp µ 1.24cP - Density 1.0g/cm 3 ρ 1.5g/cm 3 - Solubility parameter δ ( depends on phytochemicals) 16 δ 48 Mpa 1/2 Table 2 shows the model used for the target properties of blend solvent. Table 2. List of Blend Target Properties and Models used in this Work. Target property Model Partition coefficient, Log K ow Linear mixing rule Boiling point, T b Klein et al (1992)[8] Toxicity, LC 50 Linear mixing rule Stability, G mix Pinal et al (1991)[9] Solubility parameter, δ Linear mixing rule Viscosity, µ Mehrotra et al (1996)[10] Density, ρ [Yunus et al (2014)[11]] 3.3. Task (III): Design solvent blend Current practice uses a mixture of alcohol and water as solvent in extracting phytochemicals from herbs. The normal solvent used are ethanol, methanol and propanol blend with water. The mixture composition is determined through trialand-error method or from the literature. 30 solvents data and four main phytochemicals for Kacip Fatimah with their selected properties are used as input data. The data are listed in Table 3. Table 3. Input Data for Phytochemicals. Property Phytochemical δ, Mpa 1/2 T b,k Log k ow H fus, Kj/mol T m,k Kaempferol Myricetin Quercetin Rutin

8 32 S. N. H. Mohammad Azmin et al Mixture design algorithm In this study, Kaempferol which is one of the main phytochemical in Kacip Fatimah herb has been selected as the desired phytochemical to be extracted (a) Level 1: Pure component constraints In this level, two properties which are solubility parameter and partition coefficient are considered. These properties have the interrelation between solvent and phytochemicals which affect the extraction process efficiency. The other properties used are for safety and compatibility to the extraction process consideration. After considering all the constraints set in level 1, from the 870 (total combination of binary solvents = (n-1) x n, where n is number of solvent in database) possible total combinations of binary solvents, 119 binary solvents combinations have been screened which satisfy all the constraints specified in level 1. These binary solvents combination will be further screened in level (b) Level 2: Linear design constraints Linear properties which are partition coefficient, solubility parameter, density and toxicity are considered in this level. The composition range that matched the target properties boundary is obtained, followed by the determination of overall composition range for each mixture candidates. From this level, the solvent mixture candidates that matched the target properties are 36 combinations (c) Level 3: Non-linear design constraints Non-linear property which is boiling point is now considered. Fig. 2 shows the reduction number of solvent blend combination from Level 1 to Level 3. From this figure, the decomposition method shows the reduction of solvent mixture candidates in order to match the target property constraints. Therefore, this method can be used to search the optimal solvent blends that match the target properties values. After the non-linear property model is calculated, the list of candidates will be further reduced. In this level, the composition of the binary mixture listed in Level 2 was used to find the value of boiling point that match the target property as stated in Table 1. Boiling point value that did not match the target value was removed. The overall composition range that matched the boiling point is obtained. There are 17 feasible solvent mixtures after the non-linear design constraint with the properties that satisfy all the target properties are listed in Table 4. In this table, the listed solvent mixtures (S1+S2) with solvent compositions, x1 are selected based on the target properties listed in Table 1. As an example, the solubility parameter, δ for target solvent mixture is between 16 Mpa1/2 and 48 Mpa1/2. Thus, the solvent mixture listed in Table 4 must have the solubility parameter value within this range. The same goes to other properties which are partition coefficient (log kow), density (ρ), toxicity (-log LC50), and boiling point, (Tb).

A Framework for Solvent Selection Based on Herbal Extraction Process Design 33 Fig. 2. The Reduction Number of Solvent Blend Combination from Level 1 to Level 3. Table 4.

37 2.15 1.20 340.65 Methanol+water 0.77-0.18 27.97 1.37 2.16 1.21 340.65 Methanol+water 0.78-0.18 27.71 1.38 2.18 1.21 340.65 Methanol+water 0.79-0.18 27.45 1.38 2.20 1.22 340.65 Methanol+water 0.8-0.18 27.19 1.

9 A Framework for Solvent Selection Based on Herbal Extraction Process Design 33 Fig. 2. The Reduction Number of Solvent Blend Combination from Level 1 to Level 3. Table 4. Feasible Solvent Mixtures with their Properties. Mixture log δ Ρ -log µ Tb x S 1 +S 1 2 k ow Mpa 1/2 g/cm 3 LC 50 cp K Methanol+water Methanol+water Methanol+water Methanol+water Methanol+water Methanol+water Methanol+water Methanol+water Methanol+water Methanol+Etyhl acetate Methanol+Etyhl acetate Methanol+Etyhl acetate Methanol+acetic acid Methanol+acetic acid Methanol+acetic acid Methanol+acetic acid ,3-Propanediol- 2+metyhlpropanal Conclusions A systematic methodology for design of blended solvent for extracting phytochemicals from herb has been developed and was tested on the extraction of Kaempferol phytochemical from Kacip Fatimah herb. A decomposition method has been applied to solve the blending problem, where the objectives are to quickly screen out a large number of alternatives and to reduce the search space at each hierarchical step. The 17 shortlisted solvent blends in non-linear design constraints

10 34 S. N. H. Mohammad Azmin et al. needs to be checked on its stability and will be further verified with experimental study. The methodology applied can be used to design blended solvent for extracting phytochemicals from any herb where the scope and size depend on the solvent data base available and models availability in the property model library. For future work, this systematic methodology needs to be verified with different herbs as case studies. Acknowledgement This work was supported by the Research University Grant, RUG (Vote number: Q.J H44) Universiti Teknologi Malaysia,UTM and the Ministry of Education, Malaysia. This support is gratefully acknowledged. References 1. Karunanithi, A.T.; Achenie, L.E.; and Gani, R. (2005).A new decompositionbased computer-aided molecular/mixture design methodology for the design of optimal solvents and solvent mixtures. Industrial & Engineering Chemistry Research, 44(13), Conte, E.; Gani, R.; Cheng, Y.S.; and Ng, K.M. (2012). Design of formulated products: Experimental component. AIChE Journal, 58(1), Conte, E.; Gani, R.; and Ng, K.M. (2011).Design of formulated products: a systematic methodology. AIChE Journal, 57(9), Yunus, N.A.; Gernaey, K.V.; Manan, Z.A.; Woodley, J.M.; and Gani, R. (2011). Design of tailor-made chemical blend using a decomposition-based computer-aided approach. International Conference on Modeling, Simulation and Applied Optimization, Gani, R. (2004). Computer-aided methods and tools for chemical product design. Chemical Engineering Research and Design, 82(11), Conte, E.; Morales-Rodriguez, R.; and Gani, R. (2009).The virtual productprocess design laboratory as a tool for product development. European Symposium on Computer Aided Process Engineering. 7. Karimi, E.; Jaafar, H.Z.; and Ahmad, S. (2011). Phytochemical analysis and antimicrobial activities of methanolic extracts of leaf, stem and root from different varieties of Labisa pumila Benth. Molecules, 16(6), Klein, J.; Wu, D.; and Gani, R. (1992). Computer aided mixture design with specified property constraints. Computers & Chemical Engineering, 16, Pinal, R.; Lee, L.S.; and Rao, P.S.C. (1991). Prediction of the solubility of hydrophobic compounds in nonideal solvent mixtures. Chemosphere, 22(9-10), Mehrotra, A.K.; Monnery, W.D.; and Svrcek, W.Y. (1996). A review of practical calculation methods for the viscosity of liquid hydrocarbons and their mixtures. Fluid Phase Equilibria, 117(1-2), Yunus, N.A.; Gernaey, K.V.; Woodley, J.M.; and Gani, R. (2014). A systematic methodology for design of tailor-made blended products. Computers & Chemical Engineering, 66,

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