Zhenya Jia

Efficient short-term scheduling of refinery operations based on a continuous time formulation

The problem addressed in this work is to develop a comprehensive mathematical programming model for the efficient scheduling of oil-refinery operations. Our approach is first to decompose the overall problem spatially into three domains: the crude-oil unloading and blending, the production unit operations and the product blending and delivery. In particular, the first problem involves the crude-oil unloading from vessels, its transfer to storage tanks and the charging schedule for each crude-oil mixture to the distillation units. The second problem consists of the production unit scheduling which includes both fractionation and reaction processes and the third problem describes the finished product blending and shipping end of the refinery. Each of those sub-problems is modeled and solved in a most efficient way using continuous time representation to take advantage of the relatively smaller number of variables and constraints compared to discrete time formulation. The proposed methodology is applied to realistic case studies and significant computational savings can be achieved compared with existing approaches.

Centralized–decentralized optimization for refinery scheduling

This paper presents a novel decomposition strategy for solving large scale refinery scheduling problems. Instead of formulating one huge and unsolvable MILP or MINLP for centralized problem, we propose a general decomposition scheme that generates smaller sub-systems that can be solved to global optimality. The original problem is decomposed at intermediate storage tanks such that inlet and outlet streams of the tank belong to the different sub-systems. Following the decomposition, each decentralized problem is solved to optimality and the solution to the original problem is obtained by integrating the optimal schedule of each sub-systems. Different case studies of refinery scheduling are presented to illustrate the applicability and effectiveness of the proposed decentralized strategy. The conditions under which these two types of optimization strategies (centralized and decentralized) give the same optimal result are discussed.

Predictive Modeling for Pharmaceutical Processes Using Kriging and Response Surface

Powder feeding is a fundamental unit operation in the pharmaceutical industry. For the cases in which first-principle process models are unknown, such as when new powder mixture feeding operations are being evaluated, or no longer accurately describe current operating behavior, surrogate model-based approaches can be employed in order to quantify input–output behavior. In this work, two such metamodeling techniques—kriging and response surface methods—are used to predict a loss-in-weight feeder unit’s flow variability in terms of unit flowability and feed rate. Based on a comparison of predicted with experimental values, an iteratively constructed kriging model is found to more accurately capture the feeder system behavior compared with the response surface methodology. Although feeders are used as a case study in this paper, the kriging methodology is general to address other processes where first-principle models are not available.

Scheduling under Uncertainty using MILP Sensitivity Analysis

Abstract The aim of this paper is to develop an integrated framework in order to address the issue of uncertainty in short-term scheduling. The idea of inference-based sensitivity analysis for MILP problem is employed within a branch and bound solution framework to determine the importance of different parameters and constraints and to provide a set of alternative schedules for the range of uncertain parameters under consideration. An illustrative example is considered using the proposed approach and the results are compared with parametric programming and robust optimization

Scheduling under demand uncertainty using a new multiparametric programming approach

Abstract In this paper, a novel framework is developed to deal with multiple uncertain parameters on the right-hand-side (RHS) that can vary independently. The issue is also addressed using parametric mixed integer linear programming (pMILP) analysis where uncertain parameters are present on the right hand side (RHS) of the constraints. For the case of multiple uncertain parameters, a new algorithm of multiparametric linear programming (mpLP) is proposed that does not require the construction of the LP tableaus but relies on the comparison between solutions at leaf nodes. Given the range of uncertain parameters, the output of this proposed framework is a set of optimal integer solutions and their corresponding critical regions and optimal functions

Incorporation of flexibility in scheduling decision-making

Abstract Uncertainty is a very important factor in process operations. In this paper, a systematic framework is developed to address the problem of accounting for uncertainty in the scheduling decision-making process. The objectives are to increase the schedule flexibility prior to its execution and identify the important parameters and their effects into the scheduling performance. Two approaches are proposed: robust optimization and inference-based sensitivity analysis. The proposed formulation incorporates the consideration of solution robustness and model robustness. Examples are presented to illustrate the applicability of the proposed approach in batch plant scheduling.