Optimal design of reliable integrated chemical production sites

Since plants that form the process network are subjected to fluctuations in product demand or random mechanical failures, design decisions such as adding redundant units and increasing storage between units can increase the flexibility and reliability of an integrated site. In this paper, we develop a bi-criterion optimization model that captures the trade-off between capital investment and process robustness in the design of an integrated site. Design decisions considered are increases in process capacity, introduction of parallel units, and addition of intermediate storage. The mixed-integer linear programming (MILP) formulation proposed in this paper includes the representation of the material levels in the intermediate storage by means of a probabilistic model that captures the effects of the discrete, uncertain events. We also integrate a superstructure optimization with stochastic modeling techniques such as continuous-time Markov chains. The application of the proposed model is illustrated with two example problems.</p

An efficient method for optimal design of large-scale integrated chemical production sites with endogenous uncertainty

Integrated sites are tightly interconnected networks of large-scale chemical processes. Given the large-scale network structure of these sites, disruptions in any of its nodes, or individual chemical processes, can propagate and disrupt the operation of the whole network. Random process failures that reduce or shut down production capacity are among the most common disruptions. The impact of such disruptive events can be mitigated by adding parallel units and/or intermediate storage. In this paper, we address the design of large-scale, integrated sites considering random process failures. In a previous work (Terrazas-Moreno et al., 2010), we proposed a novel mixed integer linear programming (MILP) model to maximize the average production capacity of an integrated site while minimizing the required capital investment. The present work deals with the solution of large-scale problem instances for which a strategy is proposed that consists of two elements. On one hand, we use Benders decomposition to overcome the combinatorial complexity of the MILP model. On the other hand, we exploit discrete-rate simulation tools to obtain a relevant reduced sample of failure scenarios or states. We first illustrate this strategy in a small example. Next, we address an industrial case study where we use a detailed simulation model to assess the quality of the design obtained from the MILP model.</p

Data-Driven Simulation and Optimization Approaches To Incorporate Production Variability in Sales and Operations Planning

We propose two data-driven, optimization-based frameworks (simulation-optimization and bi-objective optimization) to account for production variability in the operations planning stage of the sales and operations planning (S&OP) of an enterprise. Production variability is measured as the deviation between historical planned (target) and actual (achieved) production rates. A statistical technique, namely, quantile regression, is used to model the distribution of deviation values given planned production rates. Scenarios are constructed by sampling from the distribution of deviation values and used as inputs to the proposed optimization-based frameworks. Advantages and disadvantages of the two proposed frameworks are discussed. The applicability of the proposed methodology is illustrated with a detailed analysis of the results of a motivating example and a real-world production planning problem from a chemical company