Scott J. Bury

A discrete event simulation model for reliability modeling of a chemical plant

This paper discusses a discrete event simulation model developed to identify and understand the impact of different failures on the overall production capabilities in a chemical plant. The model will be used to understand key equipment components that contribute towards maximum production loss and to analyze the impact of a change policy on production losses. A change policy can be classified in terms of new equipment installation or increasing the stock level for the failure prone components. In this paper, we present the approach used and some preliminary results obtained from available data.

Simulation optimization: a review of algorithms and applications

Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation—discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise—various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in SO as compared to algebraic model-based mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.

Data-driven individual and joint chance-constrained optimization via kernel smoothing

Abstract We propose a data-driven, nonparametric approach to reformulate (conditional) individual and joint chance constraints with right-hand side uncertainty into algebraic constraints. The approach consists of using kernel smoothing to approximate unknown true continuous probability density/distribution functions. Given historical data for continuous univariate or multivariate random variables (uncertain parameters in an optimization model), the inverse cumulative distribution function (quantile function) and the joint cumulative distribution function are estimated for the univariate and multivariate cases, respectively. The approach relies on the construction of a confidence set that contains the unknown true distribution. The distance between the true distribution and its estimate is modeled via ϕ -divergences. We propose a new way of specifying the size of the confidence set (i.e., the ϕ -divergence tolerance) based on point-wise standard errors of the smoothing estimates. The approach is illustrated with a motivating and an industrial production planning problem with uncertain plant production rates.

Bottleneck analysis of a chemical plant using discrete event simulation

This paper describes a debottlenecking study for different products in a chemical plant of The Dow Chemical Company. We used discrete event simulation to represent the chemical plant operations and to identify individual processes that limit the plant production. Our analysis successfully identified different bottlenecks for each product. The simulation will be used in future evaluations of the costs and benefits of different solutions identified for validated root causes. The simulation captures plant dynamics and can be easily leveraged to other improvement opportunities in the plant with no to little customization. In this paper, we present the general approach used for identifying the bottlenecks and the analysis results.

Optimal design of reliable integrated chemical production sites

Abstract 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.

Medium-term maintenance turnaround planning under uncertainty for integrated chemical sites

Abstract Plant maintenance poses extended disruptions to production. Maintenance effects are amplified when the plant is part of an integrated chemical site, as production levels of adjacent plants in the site are also significantly influenced. A challenge in dealing with turnarounds is the difficulty in predicting their duration, due to discovery work and delays. This uncertainty in duration affects two major planning decisions: production levels and maintenance manpower allocation. The latter must be decided several months before the turnarounds occur. We address the scheduling of a set of plant turnarounds over a medium-term of several months using integer programming formulations. Due to the nature of uncertainty, production decisions are treated through stochastic programming ideas, while the manpower aspect is handled through a robust optimization framework. We propose combined robust optimization and stochastic programming formulations to address the problem and demonstrate, through an industrial case study, the potential for significant savings.

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

Abstract 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.

Long-term turnaround planning for integrated chemical sites

Abstract An integrated chemical site involves a complex network of chemical plants. Typically, these plants interact closely, are dependent on each other for raw materials and demand for their products, and have the provision of intermediate storage tanks to help manage inventory at strategic points in the network. Disruptions in the operation of these plants can drastically affect flow of material in the site network. As a result, the choice of sequence and timing of planned periodic turnarounds, which are major disruptions, is important in order to minimize effects on profits and production. We investigate a discrete-time mixed-integer linear programming (MILP) model to perform turnaround optimization. The objective is to recommend potential schedules in order to minimize losses while satisfying network, resource, turnaround, demand, financial and other practical constraints. We propose general formulations to tackle this problem and study an industrial-size site network under various scenarios over a long-term horizon.

Product Reliability and Thin-Film Photovoltaics

Despite significant growth in photovoltaics (PV) over the last few years, only approximately 1.07 billion kWhr of electricity is estimated to have been generated from PV in the US during 2008, or 0.27% of total electrical generation. PV market penetration is set for a paradigm shift, as fluctuating hydrocarbon prices and an acknowledgement of the environmental impacts associated with their use, combined with breakthrough new PV technologies, such as thin-film and BIPV, are driving the cost of energy generated with PV to parity or cost advantage versus more traditional forms of energy generation. In addition to reaching cost parity with grid supplied power, a key to the long-term success of PV as a viable energy alternative is the reliability of systems in the field. New technologies may or may not have the same failure modes as previous technologies. Reliability testing and product lifetime issues continue to be one of the key bottlenecks in the rapid commercialization of PV technologies today. In this paper, we highlight the critical need for moving away from relying on traditional qualification and safety tests as a measure of reliability and focus instead on designing for reliability and its integration into the product development process. A drive towards quantitative predictive accelerated testing is emphasized and an industrial collaboration model addressing reliability challenges is proposed.

Evaluating production improvement opportunities in a chemical plant: a case study using discrete event simulation

This paper presents a case study on using a discrete event simulation-based approach to evaluate the proposed capacity expansion and reliability improvement opportunities at a chemical plant of The Dow Chemical Company. The simulation model was successfully used to evaluate the effect of batch size increase and automation of raw material loading on the plant productivity. In addition, the modelling work identified critical failures that were significant contributors towards production loss. Despite the strong capabilities of simulation for modelling complex system dynamics and uncertainties, there are challenges associated with use of simulation. Simulation projects for such complex systems are typically long term, and require effective stakeholder management techniques for successful execution. We discuss some Six Sigma-based best practices that aid in successful execution of such projects and for ensuring the sustainability of simulation models for evaluating future improvement efforts.