Bikram Sharda

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.

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.

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.

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.

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.

Best practices for effective application of discrete event simulation in the process industries

The application of discrete event simulation in the process industries is commonly used for the analysis of reliability and maintenance improvements. However there have been increasing applications that go beyond this traditional area of application to include evaluations for chemical plant expansions, capital investment options, cycle time reduction and safety, in presence of failure prone components. This paper will present three case studies to demonstrate the use of discrete event simulation for such applications. The first case study demonstrates the use of discrete event simulation to identify critical failure modes for a plant characterized by discrete and continuous product flow. The second study involves the evaluation of capital expansion decisions in presence of different failures and identification of critical components affecting plant throughput. The third case study shows the use of simulation of verify the designed production capacity of a subsystem in presence of different failures and operational constraints. The goal of this paper is to show the potential of discrete event simulation for such problems, and to present examples of best practices for the scoping and execution of simulation projects in the process industries.

Evaluating capacity and expansion opportunities at tank farm: a decision support system using discrete event simulation

This paper presents a discrete event simulation based Decision Support System to evaluate tank farm operations. The Decision Support System was developed in order to reduce capital expenditures and assist in decision making for assessing the impact of different improvement opportunities. The simulation based framework captures tank farm dynamics and can be easily scaled for additional products and different tank assignments/configurations. The model was successfully used to evaluate existing tank farm operations at a Freeport site of Dow Chemical company. In this paper, we discuss the general approach used for modeling tank farm operations and different output metrics generated by the Decision Support System.

Evaluating the impact of batch degradation and maintenance policies on the production capacity of a batch production process

This paper presents a case study that validated the production capacity of an industrial batch chemical process. A risk assessment review of the production system identified that different constraints and uncertainties could limit the actual production capacity of the plant to less than designed. To determine if production capacity was at risk, we developed a discrete event simulation to simulate a batch chemical production process with multiple parallel production units, interlocks in product loading steps, uncertainty in processing times caused by equipment failures, degradation of production process over time, and planned maintenance shutdowns. We evaluated the impact of variation in the degradation rate of the production process, and the impact of changes in renewal frequency on the total production capacity of the plant.

Risk analysis of turnaround reschedule planning in integrated chemical sites

Abstract Plant maintenance turnarounds constitute a large fraction of all maintenance activities in the process industries. We consider turnaround planning problems over large networks of interconnected plants. The network interactions provide an opportunity to plan and coordinate the different turnaround activities to save on annual downtime and recover the associated revenue. We propose a stochastic optimization model to quantify the risk of loss in rescheduling maintenance turnarounds that have been previously planned and compare the proposed approach to alternative approaches incorporating different production planning strategies under uncertainty. The model also provides simultaneous hedging strategies using inventories for unplanned outages. Thus, our model offers additional flexibility to previous approaches that address long- and medium-term turnaround planning problems, and explicitly incorporates plant reliability in the planning process. Through extensive computational studies, we show that proactive planning strategies that take uncertainty over multiple time periods into account offer substantial benefits over a reactive strategy.