Self-optimizing control methodology for mixed integer programming problems: a case study of refinery production scheduling

Abstract

When a disturbance is introduced into a chemical plant, measurements are taken and control actions are implemented to compensate for the effects of the disturbance decades, several techniques for CV selections have been reported. Most of these techniques require process models to determine CVs offline and largely depend on the ability to obtained and a feedback input (manipulated variable) was derived. The performance of the proposed approach was tested using case stud disturbance with the base case model giving an optimal profit of $56,696,407 while the proposed approach yields $50,523,054, translating to 10.888 % loss. The percentage loss for the second, third and fourth cas respectively. The results obtained have shown that the idea presented was able to effectively deal with the situation at hand with percentage loss within a reasonable degree ABSTRACT Problem formulation as mixed integer nonlinear programming (MINLP) is one of the most challenging task in refinery scheduling optimization. In most of the work reported in refinery scheduling, uncertainties from design point of view predominate. However, t need to consider operational uncertainties (disturbances) as they affect the accuracy and robustness of the overall schedule. This study proposed a novel approach under optimizing control (SOC) framework under uncertain conditions. The goal is to maintain global optimum by controlling the gradient of the cost function at zero via approximating necessary conditions of optimality (NCO) over the whole uncertain parameter space. A regres revenue (profit) as a function of independent variables using optimal operation data was