Homogenous multi-agent optimization for process systems engineering problems with a case study of computer aided molecular design

• A new multi-agent framework for process systems engineering problems.
• Based on efficient ant colony algorithm.
• A generalized framework for linear, nonlinear, mixed integer, convex, and non-convex optimization problems.
• A real world computer aided molecular design study for solvent selection problem in chemical industry.
• Better solutions than found by mathematical programming and stochastic optimization algorithms so far.

In this paper, we propose a novel homogenous multi-agent optimization (HMAO) framework for optimal design of large scale process system engineering problems. The platform is validated using a benchmark problems and a computer-aided molecular design (CAMD) problem. The molecular design problem is a solvent selection problem and it is formulated as a mixed integer nonlinear programming (MINLP) in which solute distribution coefficient of a candidate solvent is maximized subject to structural feasibility, thermodynamic property and process constraints. The model simultaneously determines the optimal decisions that include the size and the functional groups of the candidate solvents. In developing the HMAO framework, multiple efficient ant colony optimization (EACO) algorithms are considered as distinct algorithmic agents. We illustrate this approach through a real world case study of the optimal design of solvent for extraction of acetic acid from waste process stream using liquid–liquid extraction. The UNIFAC model based on the infinite dilution activity coefficient is used to estimate the mixture properties. The results show that quality of the objective function and the computational efficiencies are improved by a factor ranged from 1.475 to 4.137. The new solvents proposed in this work are with much better targeted thermodynamic properties compared to the solvents proposed so far in previous studies.