Zoé Périn-Levasseur

An Improved Linear Programming Approach for Simultaneous Optimization of Water and Energy

An optimization method based on Mixed Integer Linear Programming (MILP) has been developed for simultaneous optimization of water and energy (SOWE) in industrial processes. The superstructure integrates process thermal streams and optimizes the consumption of water while maximizing internal heat recovery to reduce thermal utility consumption. In this paper, additional concepts have been implemented in the superstructure to target the issues of the pulp and paper processes. Non-Isothermal Mixing (NIM) has been considered at different locations in order to reduce the number of thermal streams and decrease the investment cost by avoiding unnecessary investment on heat exchangers. The concepts of restricted matches and water tanks have been added to the superstructure to adapt it to the pulp and paper case studies. The Integer-Cut Constraint (ICC) technique has been combined with the MILP model to generate systematically a set of optimal solutions to support the decision-making for cost-effective configurations.

A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Kraft pulping mill

Abstract An optimization methodology based on Mixed Integer Linear Programming (MILP) has been developed for simultaneous optimization of water and energy (SOWE) in industrial processes. The superstructure integrates non-water process thermal streams and optimizes the consumption of water, while maximizing internal heat recovery to reduce thermal utility consumption. To address the complexity of water and energy stream distribution in pulp and paper processes, three features have been incorporated in the proposed SOWE method: (a) Non-Isothermal Mixing (NIM) has been considered through different locations to reduce the number of thermal streams and decrease the investment cost by avoiding unnecessary investment on heat exchangers; (b) the concept of restricted matches combined with water tanks has been added to the superstructure; and (c) the Integer-Cut Constraint technique has been combined with the MILP model to systematically generate a set of optimal solutions to support the decision-making for cost-effective configurations. The performance of the proposed improved MILP approach has been evaluated using several examples from the literature and applied to a Canadian softwood Kraft pulping mill as an industrial case study. The results indicate that this approach provides enhanced key performance indicators as compared to conceptual and non-linear complex mathematical optimization approaches.