Multiobjective Sustainable Operation Dynamics and Optimization

Biobased production has gained much attention in academia and industries as it can achieve environmental, social, and economic sustainability based on renewable materials. This paper addresses a sustainable operation control problem of biobased production with a particular focus on continuous process. We first formulate a dynamical model of biobased continuous production and present its optimal control problem. Furthermore, we discuss the characterization of solutions to the dynamical model and the relevant properties of the optimal control problem. This control problem is a multiobjective nonlinear optimization program. To tackle this problem, we develop a hybrid algorithm by combining a particle swarm algorithm with a multisearch procedure. We further provide a real case to validate our proposed models and algorithm. For this case, we evaluate the impact of inputs on operation dynamics, compared with experimental data. We find that the flow rate of raw materials have a higher impact on the productivity and sustainability of biobased operations than initial concentrations. Finally, we adopt the flow rate of raw materials as a control variable and identify the best operations policies. Computational results reveal that the proposed dynamical and control models are valid and that the hybrid algorithm is capable of solving multiobjective nonlinear optimization.