INTEC   05402
INSTITUTO DE DESARROLLO TECNOLOGICO PARA LA INDUSTRIA QUIMICA
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
AN MINLP FORMULATION FOR PLANNING QUALITY-SENSITIVE CRUDE OIL SUPPLIES THROUGH LONG-DISTANCE PIPELINES
Autor/es:
VANINA G. CAFARO; DIEGO C. CAFARO; PEDRO C. PAUTASSO; JAIME CERDÁ
Lugar:
Bahía Blanca
Reunión:
Congreso; IX Congreso Argentino de Ingeniería Química (CAIQ 2017); 2017
Institución organizadora:
Asociación Argentina de Ingeniería Química
Resumen:
 Inthis work we present a novel mixed-integer nonlinear programming (MINLP) formulationfor planning crude oil supplies to an oil refinery. We precisely monitor keycomponent concentrations keeping oil contamination below threshold values. The proposedMINLP formulation is based on a hybrid approach, combining the potentials ofslot-based and general precedence continuous-time representations. Generalprecedence sequencing variables are used to coordinate incoming/outgoing flowsto/from every tank, and we simultaneously address the scheduling of along-distance pipeline sequentially transporting crude oil batches from storageto charging tanks, following a slot-based scheme. We determine the bestsequence and timing of transportation operations in pipeline and tanks. Basic inputdata consist of the amount of oil and composition in arriving vessels andtanks, the total demand of crude oil to be satisfied, flow-rate limits and costcoefficients. Our novel approach is able to precisely calculate inventoryholding costs while it constitutes, to the best of our knowledge, the firstmethod proposed in the literature that does not require rigorous inventorytracking over time for so doing. We propose an MINLP solution algorithm that sequentiallysolves a couple of mathematical programming formulations: (a) an MILPcontinuous-time model that is a tight approximation of the MINLP, and (b) anNLP model that includes the nonlinear terms arising in the key component massbalances and inventory carrying cost calculation. Compared to previouscontributions, up to 28% cost savings are obtained in much shorter CPU times.