The goal of this article is to highlight how: Supervised machine-learning and the Internet of Things can be used to develop a soft sensor that can enable the plant operator to continuously monitor product analysis, as well as gain insights into the root cause of off-spec product—in this work, the off-spec product is distillate. Also, this article will show how reinforcement learning can be used to train an AI agent to operate the plant. 

Accurate grab sampling practices are necessary to validate the quality of chemical products with precise compositions. Operators must follow proper procedures to avoid compromising the sample. Otherwise, the result may be an inaccurate analysis of their product, leading to quality lapses and potentially costing a facility thousands of dollars in revenue.

While approximately 70% of ammonia production is used to make fertilizers, it is also identified as the largest carbon dioxide (CO2)-emitting chemical industry process. This article examines several novel technologies to improve the sustainability of the ammonia production process.

This article will mainly focus on horizontal flare systems and grade-level burn pits. Design considerations, operational challenges and several lessons learned will be discussed. 

This article presents a review of a failure that happened in a hairpin heat exchanger type on the shell side in a H2 (H2) reformer unit.

Throughout refineries, petrochemical plants and offshore oil and gas platforms, personnel are often required to work near equipment that operates at high temperatures. Protecting those workers from skin-contact burns has typically involved two approaches: implementing physical barriers and covering the hot assets with insulation and cladding. This article details how thermal insulative coatings can be used to improve safety and guard against corrosion.