Quasi-Random Sampling-Enhanced Metaheuristic Algorithms for CAMD-Based Solvent Selection in Octacosanol Extraction

This study develops a computer-aided molecular design (CAMD) framework for selecting solvents to extract octacosanol from multicomponent sugarcane wax. The solvent-selection problem is formulated as a mixed-integer nonlinear programming problem in which candidate solvents are assembled from UNIFAC functional groups and evaluated using the net distribution coefficient and net solvent selectivity. Four metaheuristic solvers—ant-colony optimization (ACO), simulated annealing (SA), efficient ant-colony optimization (EACO), and efficient simulated annealing (ESA)—are compared in terms of solvent quality and computational efficiency. EACO and ESA replace selected pseudo-random samples with Hammersley sequence samples to improve multidimensional sampling uniformity. All four solvers identify the same highest-ranked candidate, while ACO and EACO require substantially fewer objective-function evaluations than SA and ESA. The SA-based methods provide greater diversity among lower-ranked candidates. Relative to their conventional counterparts, EACO reduces the computational cost by 14.2% and ESA by 16% while preserving the leading solvent candidates. Overall, the CAMD framework consistently identifies promising candidates, including ethane and propanal, for octacosanol extraction, and quasi-random sampling improves the efficiency of both metaheuristic approaches.