Jeffrey E. Arbogast

An incremental approach using local-search heuristic for inventory routing problem in industrial gases

Abstract In this paper we solve the inventory routing problem (IRP) occurring in industrial gas distribution where liquefied industrial gases are distributed to customers that have cryogenic tanks to store the gases on-site. We consider a multi-period inventory routing problem with multiple products assuming deterministic demand rates and the proposed model is formulated as a linear mixed-integer program. We propose an incremental approach based on decomposing the set of customers in the original problem into sub-problems. The smallest sub-problem consists of the customer that needs to be delivered most urgently along with a set of its neighbors. We solve each sub-problem with the number of customers growing successively by providing the solution of the previously solved sub-problem as an input. Each sub-problem is then solved with a randomized local-search heuristic method. We also propose an objective function that drives the local-search heuristics toward a long-term optimal solution. The main purpose of this paper is to develop a solution methodology appropriate for large-scale real-life problem instances particularly in industrial gas distribution.

Design for Process Safety – A Perspective

Abstract Key safety-related elements in process design are discussed: inherent safety, Hazard and Operability (HAZOP) analysis, process integration with improved safety, Model- Predictive Control (MPC) for improved control and risk reduction, identification of special causes, and multi-objective optimization. Recent developments in data mining to extract risk estimates from extensive near-miss records (e.g., alarm databases) are reviewed. Building upon these techniques, the development and application of statistical methods to both predict abnormal events (generally resulting in alarm activations) with leading indicators and to diagnose their special (e.g., root) causes follows. In our view, the goal is to develop and implement techniques and safety systems to better provide plant operators actionable (e.g., valid, timely, non-redundant) information on emerging abnormal situations. These improved techniques should account for today’s dynamic operation of process plants, which are more integrated and therefore have increased multivariable interactions that must be taken into account.

Understanding rare safety and reliability events using transition path sampling

Abstract In the chemical and process industries, processes and their control systems are typically well-designed to mitigate abnormal events having potential adverse consequences to human health, environment, and/or property. Strong motivation exists to understand how these events develop and propagate. These events occur so rarely that statistical analyses of their occurrences alone are incapable of describing and characterizing them − especially when they have not yet occurred. Moreover, the use of process models to understand such rare events is hampered by the orders of magnitude separating the frequencies with which reliability and safety events (years to decades) occur and the duration over which they occur (minutes to hours). To address these challenges, we adapt a Monte-Carlo based, rare-event sampling technique, Transition Path Sampling (TPS), which was developed by the molecular simulation community. Important modifications to the TPS technique are needed to apply it to process dynamics, and are discussed herein.