Effective scheduling of complex process-shops using online parameter feedback in crude-oil refineries

Integrated scheduling optimization comprising the battery limits of crude-oil refineries is a challenging problem to be solved as it includes decisions concerning the quantity and quality of the crude-oil feedstocks and final products (such as fuels and petrochemicals) as well as production, processing or make-side of the refinery flow network. So far, the literature on crude-oil scheduling optimization covered the problem from the crude-oil unloading and storage up to the distillation straight-run streams. To go further, this research extends the scope of the problem from the raw material deliveries up to product liftings through the refinery process-shop by using closed-loop, online and routine process feedback data from field and laboratory measurements for better process predictions, integrated within the scheduling cycle. For such engine, past routine operating data calibrates gains and biases as y_measured = gain × y_model + bias, whereby the data updating in y considers both process yields and variables such as throughputs, flows, holdups and properties, whose effects propagate throughout the process network. Parameter feedback is applied, after data reconciliation computation, in a complete crude-oil refinery blend scheduling problem considering real tank topology, cascaded distillation towers, process-shops and blend-shops to effectively optimize the complex process system. The feedback strategy is solved within an iterative mixed-integer linear and nonlinear programming (MILP-NLP) decomposition by updating NLP results of process-shop's yields and properties, and recipes of blend-shops in the next MILP solution until convergence is achieved.