Generalized capital investment planning of oil-refineries using MILP and sequence-dependent setups

• Published in: Computers & chemical engineering 2015-09, Vol.80, p.140-154
• Main Authors: Menezes, Brenno C., Kelly, Jeffrey D., Grossmann, Ignacio E., Vazacopoulos, Alkis
• Format: Article
• Language: English
• Subjects: Computer simulation; Decisions; Financing; Investment; Investment planning; Maintenance; Nonlinearity; Oil-refinery production; Process design synthesis; Retrofitting; Scheduling; Sequence-dependent changeovers
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2015.05.013

•We generalize the modeling by combining planning balances with scheduling concepts.•Nonlinearities are approximated to linearized operations, activities, or modes.•We use semantic variables such as flows, holdups, yields, setups and startups.•Capacity and capital are re-formulated as flows in a scheduling environment.•We unify the three CIP problems of repair, retrofit and revamp within the same framework. Due to quantity times quality nonlinear terms inherent in the oil-refining industry, performing industrial-sized capital investment planning (CIP) in this field is traditionally done using linear (LP) or nonlinear (NLP) models whereby a gamut of scenarios are generated and manually searched to make expand and/or install decisions. Though mixed-integer nonlinear (MINLP) solvers have made significant advancements, they are often slow for large industrial applications in optimization; hence, we propose a more tractable approach to solve the CIP problem using a mixed-integer linear programming (MILP) model and input–output (Leontief) models, where the nonlinearities are approximated to linearized operations, activities, or modes in large-scaled flowsheet problems. To model the different types of CIP's known as revamping, retrofitting, and repairing, we unify the modeling by combining planning balances with scheduling concepts of sequence-dependent changeovers to represent the construction, commission, and correction stages explicitly in similar applications such as process design synthesis, asset allocation and utilization, and turnaround and inspection scheduling. Two motivating examples illustrate the modeling, and a retrofit example and an oil-refinery investment planning problem are also highlighted.