Ankur Pariyani

Design of heat exchanger networks using randomized algorithm

A randomized algorithm with stream splitting for design of heat exchanger networks is presented in this work. The algorithm has provisions for splitting any one of the process streams. We have studied three benchmark problems taken from literature. The results obtained from these studies clearly indicate the strength of the randomization method in finding a cost-effective network. This random search method can find better networks, which are sometimes unnoticed by other optimization techniques. The simplicity of this method as well as the networks obtained is an additional attractive feature, which should encourage the designers to use it. From the results of this study, randomization is recommended as a reliable check for designing heat exchanger networks.

Assessment of Catastrophe Risk and Potential Losses in Industry

This paper describes the potential contribution of near-miss management systems to improving company profitability and reducing the frequency and severity of major industrial accidents. The near-miss concept has long been understood in different industries, as examples in this paper illustrate. However, what has been largely missing is the integration of near-miss management into the culture and day to day operations in a manner that underlines the critical connections between near-misses and behavior. Often, near-miss management has played an ex post forensic role in risk management rather than an alerting one, summarizing leading indicators and precursors of hazardous conditions. This paper describes several strands of recent research that aim to correct this and to make near-miss management an organic element of Enterprise Risk Management. In this respect, a new concept, “potential safety profit loss”, is introduced to calculate the potential monetary losses due to unexpected shutdowns and accidents.

Improving Process Safety and Product Quality using Large Databases

Abstract This paper introduces a novel modeling and statistical framework (based on Bayesian theory) that utilizes extensive distributed control system and emergency shutdown databases, to perform thorough risk and vulnerability assessment of chemical/petrochemical plants. Quality variables are utilized, in addition to safety (or process) variables, to enhance both process safety and product quality . To effectively achieve these objectives, new concepts of abnormal events and upset states are defined, which permit the identification of near-miss events from the databases. The databases for a fluid catalytic cracking unit at a major petroleum refinery are used to demonstrate the application and performance of the techniques introduced herein. The results show that with the novel utilization of near-miss data, one can perform robust risk calculations using both product-quality and safety data.