An MINLP Formulation for Planning Quality Sensitive Crude Oil Supplies through Long-Distance Pipelines

VANINA G. CAFARO; PEDRO C. PAUTASSO; JAIME CERDÁ; DIEGO C. CAFARO

Bahía Blanca

Congreso; IX Congreso Argentino de Ingeniería Química (CAIQ); 2017

AAIQ Asociación Argentina de Ingenieros Químicos - PLAPIQUI

In this work we present a novel mixed-integer nonlinear programming (MINLP) formulation for planning crude oil supplies to an oil refinery. We precisely monitor key component concentrations keeping oil contamination below threshold values. The proposed MINLP formulation is based on a hybrid approach, combining the potentials of slot-based and general precedence continuous-time representations. General precedence sequencing variables are used to coordinate incoming/outgoing flows to/from every tank, and we simultaneously address the scheduling of a long-distance pipeline sequentially transporting crude oil batches from storage to charging tanks, following a slot-based scheme. We determine the best sequence and timing of transportation operations in pipeline and tanks. Basic input data consist of the amount of oil and composition in arriving vessels and tanks, the total demand of crude oil to be satisfied, flow-rate limits and cost coefficients. Our novel approach is able to precisely calculate inventory holding costs while it constitutes, to the best of our knowledge, the first method proposed in the literature that does not require rigorous inventory tracking over time for so doing. We propose an MINLP solution algorithm that sequentially solves a couple of mathematical programming formulations: (a) an MILP continuous-time model that is a tight approximation of the MINLP, and (b) an NLP model that includes the nonlinear terms arising in the key component mass balances and inventory carrying cost calculation. Compared to previous contributions, up to 28% cost savings are obtained in much shorter CPU times.