ABSTRACT
In the process industry, disruptions directly affect both downtime and efficiency. Anomalies in any automated process have a direct and devastating effect on customer-focused values, high rejection rates of raw materials, and overall production costs, necessitating specialized care and methods for effective resolution. An incredible example of such a system is the human immune system, which responds to viral and bacterial infections by deploying B and T-cells that either directly kill the invader (Phagocytosis) or neutralize it, respectively. Motivated by these findings, this study suggests adopting a model similar to the HIS to be utilized in the business world to deal with disruptions. For this reason, the Artificial Immune System employs ontologies based on the immune system (AIS). As a model, it effectively isolates sources of disruption and generates responses to them mechanically. Moreover, the proposed model could dynamically deal with interruptions by assigning weights to the variables involved. This research utilizes weights to evaluate the overall response. Protégé was employed for ontology development (software). The research was conducted in a lab setting using a test bed, with 15 input/output (IO) variables studied for their effects on process downtime, system efficiency, and customer-centric value.
