(9f) Selection of Greener Solvents Using Computer-Aided Molecular Design (CAMD) to Extract Octacosanol from Filter Press Mud of Sugarcane

Generally, sugar industries generate large amounts of solid wastes like bagasse, rind, press mud, and bagasse fly ash. Filter press mud is obtained during the filtration of the cane juice performed after the clarification process. Sugarcane mills in Brazil, India, and China account for 75% of the global supply of filter press mud [1]. In addition to esters, aldehydes, and carboxylic acids, acids, press mud consists of high-value nutraceuticals like policosanols, which are aliphatic alcohols consisting of an even number of carbon atoms (C26-C32), of which 1-octacosanol is the most abundant, and are known to have appreciable health benefits. Octacosanol has been associated with health benefits such as inflammatory diseases, cholesterol-lowering effect, anti-Parkinsonism, etc., [2]. For these reasons, perhaps, pure 1-octacosanol is a high-economic-value nutraceutical, and considerable interest exists in separating octacosanol from the mixture of compounds in filter press mud. This work assumes that the octacosanol purification will start with a solvent extraction process. We focus on arriving at a rational choice for the solvent for extraction of octacosanol from filter press mud.

Initially, this paper begins with a review of the compounds reported to be present in sugarcane solid wastes. A study of the analysis of various solvents using Hansen's solubility theory is presented next. The main contribution of the paper consists of exploring the space of solvents, characterized by the various functional groups, via CAMD and optimization in order to obtain those solvents that exhibit the highest solubility. An optimization approach to CAMD involves a combinatorial optimization method, as the resulting problem involves the combinatorial explosion of alternatives. This poses a challenge to optimization techniques. The possibility of obtaining local minima or maxima is very high in such problems [3]. Some of the biomimick optimization techniques, like ant colony optimization, show a higher probability of obtaining global solutions. We are using an efficient ant colony optimization technique to solve the CAMD problem of solvent selection.

Octacosanol in Sugarcane

Octacosanol is one of the compounds present in policosanols, which are a mixture of linear α-carbon fatty alcohols. It is hydrophobic in nature ("fatty" alcohol), which implies it is insoluble in water [4]. Attard et al. [5] reported that rind is rich in saturated fatty alcohols (i.e., policosanols), saturated fatty aldehydes, and a little in wax esters and fatty acids. Table 1 shows some of the compounds rich in rind.

In this study, we assume that the policosanols present in rind are transferred to the juice during crushing and subsequently manifest as filter press mud during clarification. Thus, the compounds reported in Table 1 may be assumed to be the primary constituents of filter press mud, of which octacosanol must be recovered and purified.

Recent studies on the extraction of policosanols make use of solvent extraction with a Soxhlet apparatus and supercritical fluid extraction [5]. Supercritical fluid extraction is a high-cost process due to which its application is limited [6]. Subcritical liquefied extraction, an advancement of supercritical fluid extraction, costs lower energy and is a lower-pressure process. A recent study with this technique with dimethyl ether as solvent yielded 249.21 ± 13.9 mg of C28 per 100 g of the crude extract [6].

Since the compounds present in policosanols are homologous (i.e., differ only by –CH₂–), their properties are similar to each other. Therefore, we would at least try to recover the maximum amount of octacosanol from the filter press mud using a solvent extraction process. Thus, the ideal solvent should have high solubility for octacosanol to yield high concentrations of the extract and differential affinity for octacosanol so that the extract also exhibits high purity. If high purity is attained via the solvent, then the burden on the downstream purification of octacosanol will be lower.

Solvent Selection: Hansen Solubility Theory

Conventionally, the solubility of solvents is determined by experimental tests made in laboratories. Although this method is reliable, it is based on trial and error, and a better solvent may be missed out. Also, this method does not explicitly account for constraints like environmental and health safety. Another rational approach is based on the contribution of individual functional groups and exploits the availability of a database of the properties of these individual groups [3]. Hansen solubility parameter model is a method where the solubility parameter (δ), which was coined by Hildebrand and Scott [7], serves to determine the solubility of a given solute in a given solvent [3]. Solubility δ is the square root of the cohesive energy density, (E/V)^1/2, where E denotes the energy of vaporization and V denotes the molar volume of pure solvent [8].

The square of the solubility parameter (δ²) is the sum of squares of three other terms which denote dispersion (δ_d²), polarity (δ_p²), and hydrogen bonding (δ_h²) between groups of molecules. This denotes the sum of individual cohesion energy densities equals total cohesion energy density.

Based on the solubility parameters of the two compounds, a new parameter distance (Ra) was introduced by Skaarup to study the interactions between the two compounds. The three-dimensional solubility parameters are calculated based on the types of groups present in the compound. Unless the distance between solute and solvent is less than that of solute and raffinate, solute molecules are immiscible (or simply insoluble) in the selected solvent. Liquids having close values of solubility parameters are soluble with each other, which in turn results in high distribution coefficient. High values of distribution coefficient get rid of large size of extracting equipment, and the requirement of recycling solvent lowers too [3]. Manuel Diaz de los Ríoset al. [9] came up with this technique, Hansen solubility theory, to obtain a fraction of sugarcane wax using ethanol as solvent. They concluded that ethanol was the best solvent at high temperatures because the temperature effect and other thermodynamic factors, namely dissolution and mixing, revealed that affinity to attract increased. In addition, a mixture of ethanol and water are taken in 5% v/v and 10% v/v, respectively, was capable of extracting aldehydes and alcohols with increasing temperature. This important study showed the path to another application of Hansen's solubility theory and the dependence of temperature and other thermodynamic factors in this theory. However, the study was limited to extracting a fraction of sugarcane wax using ethanol would be possible only under certain conditions. And this was not carried forward to verify other possible solvents that might work even under normal conditions.

The Hansen solubility theory proposes solvents made up of different groups like -CH₃, -CH_2­-, -OH, -COO, -COOH, >C=C<, -O-, etc. that can make up to large combinations of solvents. Computer-aided Molecular Design (CAMD) is the reverse use of the group contribution method, like the Hansen solubility theory, to generate molecules with desirable properties. Selecting the optimal solvent optimization provides the platform where combinatorial optimization algorithms can be used to achieve the requirement.

Efficient Ant-Colony Optimization

A combinatorial optimization problem can be solved using an ant colony optimization (ACO) algorithm or any other metaheuristic approaches like simulated annealing and genetic algorithm [10]. However, the area of research to extend this algorithm to continuous and mixed variable non-linear problems has been extensively widened. Such research led to the development of a new variant of ACO called the efficient ant-colony optimization (EACO) method. [10]

ACO algorithm was developed by bio-mimicking the behavior of real ants that are in search of food. An ant releases a chemical called pheromone on its path and returns to the initial point. The same would help the other ants to track down the path toward the food source. There would be many paths, and as time flies concentration of pheromones in less frequently used paths decreases. This would automatically lead to shorter paths, and finally, the shortest path will be discovered.

Now, based on this idea and the inclusion of Hammersley sequence sampling (HSS), the EACO algorithm was developed. This method can solve convex and concave optimization problems and is highly efficient compared to ACO alone. In ACO, the solution archive is first initialized and generates new solutions iteratively until it finds the best one. This solution archive produces a probability distribution of the promising solutions across the search space. The performance of this algorithm greatly depends on initializing the solution archive. Because of HSS's in-built multi-dimensional uniformity, incorporating it in the ACO algorithm helps to avoid clustering of the initial solution archive and movement of decision variables. [10]

Conclusion

Solvent selection involves finding an optimal solvent that maximizes properties like distribution coefficient and solvent selectivity. The solvent can be high boiling or low boiling. To make the solvent environmentally benign, we are including environmental constraints in the problem. In this paper, we are using an optimization approach based on efficient ant colony optimization to solve the real-world problem of finding optimal solvents to extract octacosanol from filter press mud of sugarcane. We plan to verify the optimal solvent selection results obtained by CAMD theory using laboratory scale experiments.