Brenno C. Menezes

Phenomenological Decomposition Heuristic for Process Design Synthesis of Oil-Refinery Units

Abstract We propose a mixed-integer nonlinear optimization for process design synthesis of oil-refinery units that includes crude-oil mixing, unit processing and product blending. The quantity-logic-quality phenomena involving a non-convex mixed-integer nonlinear problem is decomposed into a two-stage stochastic programming model with complete recourse considering, in a first stage, quantity and logic variables in a mixed-integer linear model and, in a second stage, quantity and quality variables in a nonlinear programming formulation. Iteratively, nonlinear models of each demand scenario are restricted by the multi-scenario process design results. An industrial-sized example that is not solved in a full space model demonstrates our tailor-made decomposition scheme, which yields within 5% gap between the first and the average second stage results.

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

Abstract 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 CIPs 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.

Enterprise-Wide Optimization for Operations of Crude-Oil Refineries: Closing the Procurement and Scheduling Gap

Abstract We propose a quantitative analysis of an enterprise-wide optimization for operations of crude-oil refineries considering the integration of planning and scheduling to close the decision-making gap between the procurement of raw materials or feedstocks and the operations of the production scheduling. From a month to an hour, re-planning and re-scheduling iterations can better predict the processed crude-oil basket, diet or final composition, reducing the production costs and impacts in the process and product demands with respect to the quality of the raw materials. The goal is to interface planning and scheduling decisions within a time-window of a week with the support of re-optimization steps. Then, the selection, delivery, storage and mixture of crude-oil feeds from the tactical procurement planning up to the blend scheduling operations are made more appropriately. The up-to-down sequence of solutions are integrated in a feedback iteration to both reduce time-grids and as a key performance indicator.

Decision Automation for Oil and Gas Well Startup Scheduling Using MILP

Abstract A novel approach to scheduling the startup of oil and gas wells in multiple fields over a decade-plus discrete-time horizon is presented. The major innovation of our formulation is to treat each well or well type as a batch-process with time-varying yields or production rates that follow the declining, decaying or diminishing curve profile. Side or resource constraints such as process plant capacities, utilities and rigs to place the wells are included in the model. Current approaches to this long-term planning problem in a monthly time-step use manual decision-making with simulators where many scenarios, samples or cases are required to facilitate the development of possible feasible solutions. Our solution to this problem uses mixed-integer linear programming (MILP) which automates the decision-making of deciding on which well to startup next to find optimized solutions. Plots of an illustrative example highlight the operation of the well startup system and the decaying production of wells.