GOALI: Long-term Planning of Plant-wide Recovery and Treatment Options

University Of Illinois At Chicago, Chicago IL

Investigators

Abstract

ABSTRACT PI: Andreas Linninger and Richard Colberg Institution: University of Illinois at chicago Proposal Number: 0090370 Planning of waste reduction and pollution prevention efforts for multi-purpose batch manufacturing sites is concerned with the mindful use of resources such as raw materials, mass separating agents and energy to support the production of chemicals. It is a dynamic open-ended problem, since strategies should anticipate emerging trends in future business operations, availability of new technologies beyond current manufacturing practices or environmental concerns. Uncertainty stems from a continuous sequence of production campaigns, each lasting from weeks to months. Plant-wide waste management typically builds on existing site infrastructure such as solvent recovery plants, incinerators, chemical treatment technologies, and biological treatment. The retrofit situation in the presence of uncertainty contrasts the deterministic grass-roots design approach predominant in dedicated plants used in the bulk commodity business. This research consists of developing optimal design methodology for plant-wide waste management for a fixed planning horizon. Due to the multi-faceted nature of the problem, a synergistic academic-industrial collaboration (GOALI project) is planned. The PIs plan to develop a new technique they call "combinatorial process synthesis," which uses a two-phase solution approach. In phase one, superstructure generation, a knowledge-based reasoning mechanism searches for a superstructure of technically feasible recovery and treatment options. Non-linear balance and constitutive equations are enforced to safeguard the consistency of the evolving state task network. In step two, superstructure optimization identifies plant-wide waste management strategies subject to plant-specific logistic, capacity and emission constraints. Optimal strategies also suggest investment decisions such as new recycle technologies or treatment options to simultaneously increase economic and ecological performance or to address more stringent environmental standards. The major advantage of the combinatorial process synthesis method lies in its ability to render an algorithmic solution to open-ended problems via a combination of informed search and mathematical programming.