Performance Analysis of a Soap Plant using Machine learning

Shakuntla Singla; Shilpa Rani; Sonia; Diksha Mangla; Savita Garg

doi:10.5269/bspm.76353

Shakuntla Singla Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India https://orcid.org/0000-0002-5713-2982
Shilpa Rani Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India https://orcid.org/0009-0008-8245-4036
Sonia Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India https://orcid.org/0009-0006-9721-6116
Diksha Mangla Department of Mathematics and Humanities, MMEC, Maharishi Markandeshwar (Deemed to be University) Mullana, Ambala, India. https://orcid.org/0009-0008-8921-449X
Savita Garg Department of Mathematics,Mukand Lal National College, Yamunanagar-135001, India. https://orcid.org/0009-0002-1916-2491

Resumo

This research presents the development of a reliability model for availability analysis of the soap operation, in which the main unit can function in a reduced condition following partial failure.The soap Plant essentially accommodates of 4 subsystems especially boiler, mixer, vacuum chamber and metal detector. Packaging has sub-units. The reliability model for soap industry availability is constructed, consisting of three non-identical units, and a main unit able to working in a diminished state with incomplete failure. The main unit can fail partially, resulting in an up-state, partially failed state, or completely failed state. In the event of a partial failure, the system can function at a reduced capacity. The repair of the unit and the treatment of the server are considered perfect. Machine learning is used to formulate and solve the exponential problem involving failure and repair rates. Tables and charts are likewise created to explain the system's practical trend using specific situations.

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