A DISCRETE EVENT SIMULATION MODEL FOR RELIABILITY MODELING OF A CHEMICAL PLANT

Abstract

ABSTRACT 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. INTRODUCTION Chemical plant operations typically consist of a large number of components with complex interactions and numerous failure modes. For plants that are running at a "soldout" capacity, downtime means significant production and sales losses. To run the plant with minimum downtime, it is necessary to understand the critical components within the plant and implement new components, inventory control and preventive maintenance policies for the critical components. This paper discusses a discrete event simulation model being developed to understand and identify key failure components for a chemical plant. The chemical plant considered here produces more than 15 different types of products, consists of ~40 different subsystems (such as reactors, wash tanks, refining system) and there are more than 250 different types of component failures (based on historical data), which occur in different subsystems. Based on historical data, 36% of the production losses were due to equipment failures. To maximize the plant production, a study is being carried out to identify critical subsystems and their individual components that contribute towards significant production loss. This study will also help in understanding the effect of change policies in terms of new component installation and inventory control policies for reduction in production loss. The DES modeling of this chemical plant operation presents challenges as it involves both continuous and discrete flow of material in the plant. A barrier to successful execution of a study like this is scenario overload. To efficiently execute the key task of identifying the critical components, we designed a systematic approach. After model verification and validation, the simulation model will be first used to see a "base case" production against different products without any failures. This step will define the maximum attainable production for each product without failures. The model will then be run by considering failures for a particular subsystem (for instance, reactor system). After running the simulation model by considering failures in each subsystem, a Pareto analysis will be carried out to determine which subsystems are critical. Within each subsystem, the individual components will then be evaluated to identify components causing more frequent and costly downtimes. These components can then be analyzed for change policies such as implementing new designs or changing the inventory control policies. This systematic approach examines at the system hierarchy from the outside in, instead of an exhaustive search considering each failure. This reduces the number of possible simulation scenarios and generates data that are easier to understand and evaluate. The paper is organized as follows. Section 2 provides a brief overview of the production process. Section 3 describes the DES simulation modeling approach, section 4 provides the preliminary results and section 5 presents the summary and future work for this project. PROCESS OVERVIEW The operations of the chemical plant being considered can be subdivided into following main steps. Note the combination of discrete (batch) and continuous processing steps. 1. Raw product loading (discrete) 2. Raw product mixing (discrete) 3. Reaction (discrete) 4. Intermediate storage 1 (discrete) 5. Raw product washing-(continuous