Disruptions have a direct impact on the process downtime and efficiency pertaining to process industry. Anomalies, in any automated process, have an impeccable impact on consumer-centric values, high rejection of raw materials and cost, thus demanding special attention and techniques
to be efficiently dealt with. Human immune system (HIS) presents an astounding example of such system, wherein the disruptions caused by viruses and bacteria are addressed by deploying B and T cells, which either destroys the pathogen (virus or bacteria) by killing it (Phagocytosis) or renders
it harmless. Inspired from that, this research proposes a model analogous to HIS for industrial applications, to deal with disruptions. Addressing that, Immune based ontologies are developed for artificial immune system (AIS). This model works on the disruption detection and isolation with
automatic response generation. Furthermore, the proposed model is capable of handling the disruptions in a dynamic way via weight assignment. The final reaction is assessed based on the assigned weights. Ontology was developed using Protégé (software). Experimentation was carried
out in a controlled laboratory environment on a test bed by analysing 15 input/outputs (IOs) influencing the process downtime, system efficiency and consumer centric value.
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