Waste Solvents to Trash Haulers: Lessons Learned from Hazardous Waste Accidents

Abstract

Process safety management (PSM) of hazardous chemicals is a core tenet of the chemical, oil, and gas industries. Organizations in these industries have increasingly woven the elements of PSM into their strategic vision, day‐to‐day operations, and interactions with contractors and vendors. This cultural evolution has, in part, been driven by major accidents in these fields. In these industries, the chemicals, their hazardous properties, and their potential interactions, are usually either well‐defined or capable of being well‐defined. Control and mitigation strategies for those chemicals can be developed based on this available knowledge. In contrast, companies that handle wastes have experienced catastrophic accidents due to the ill‐defined nature of the hazards. This industry handles the cast‐offs, dregs, and refuse, which makes identification of the hazards and prevention of incidents more challenging. How do you implement a PSM program when your input streams can vary due to processes at a plant you do not control or monitor? Does the Environmental Protection Agency (EPA) Resource Conservation and Recovery Act (RCRA) provide sufficient guidance of safely handling of wastes? The overriding theme in the incidents is that PSM is more complex to apply to unknown, mixed, and uncontrolled chemicals, both solid and liquid. To assist companies that handle wastes, this paper provides several case studies of fires and explosions involving multiple waste material handling processes at different facilities. The incidents involved numerous matrices from solid industrial wastes, EPA RCRA hazardous wastes, to waste solvents. An analysis of the lessons learned will be presented and used to provide realistic guidelines to reduce safety risks based on a pragmatic implementation of process safety management principles. The paper will also compare OSHA (Occupation Safety and Health Administration) PSM with EPA RCRA approaches to characterizing wastes versus managing the hazards, and will emphasize those elements that played critical roles in the accident case studies. © 2018 American Institute of Chemical Engineers Process Process Saf Prog 37:427–441, 2018

Inherently ill‐defined nature of waste: Fatal outdoor poisoning of hazmat waste collection driver ‐ lessons learned

“…This poses practical difficulties for prospective assessment of risk because a chemical reactivity hazard is often a function of the concentrations of constituents within the waste 13 . Because of its ill‐defined nature, managing waste is inherently more challenging than managing typical chemical process hazards 14 . A study of 142 incidents reported to UK HSE caused by unintentional mixing of incompatible waste chemicals found that 62% of the incidents occurred at waste generator sites.…”

“…13 Because of its illdefined nature, managing waste is inherently more challenging than managing typical chemical process hazards. 14 A study of 142 incidents reported to UK HSE caused by unintentional mixing of incompatible waste chemicals found that 62% of the incidents occurred at waste generator sites. The practice of bulking of batches, that is, the placing of smaller quantities of what should be similar materials into larger containers, accounted for approximately 70% of incidents at waste generator sites.…”

A New Approach for Liquid Scanners to Determine Flammable Liquid Concentration in Solutions

“…Hazardous chemical reactivity events have been conducted and lessons learned from these cases as presented in [2]. In most cases, oxidisers have caused these fires to start or have contributed to the increasing coverage of the fires [3]. The mixing of incompatible liquids during the use of chemical liquids found in small containers with open covers showed that accidents and fires occurred as a result of accidental spillage and contamination [4].…”

“…The discriminant function is obtained from previously known units of group membership, and then this function is used to determine which group will be assigned to new units with unknown group membership. Using a score function defined by the algorithm, linear coefficients that give the highest values in the function are found (3). The aggregated covariance matrix is given in (4).…”