Achieving environmental sustainability requires minimizing energy consumption and waste generation. Batch chemical industries are prompted to increase productivity and utilize energy more efficiently. However, in contrast to continuous processes, batch process operations are intrinsically time dependent and the multiscale nature of batch operation posts complications in implementing classical effective waste- and energy-minimization strategies. Given the complex nature of batch plants, a systematic way of identifying and evaluating suitable operating strategies is essential. In this work, we present a multiobjective optimization-based approach that integrates a detailed model of the batch process and techniques of experimental design and evolutionary optimization. The proposed concept is applied to the batch production of fatty acid methyl esters (biodiesel). The optimization of the temperature control of this process takes into account objective functions related to purity of the product, batch time, energy usage and profit. The Pareto-fronts generated by full factorial experiment and by a multiobjective evolutionary algorithm (NSGA-II) show how the objectives are correlating or conflicting. The visualization of these fronts and the optimal temperature trajectories supports the engineers and the operators to find the best trade-off among the non-dominated solutions.
ASJC Scopus subject areas
- Environmental Chemistry
- Environmental Engineering
- Management, Monitoring, Policy and Law