Development of a Fuzzy Risk Model for Criticality Analysis of TSP Complex Limited

K. Ahsan, T. Hassan, M. M. Rahman, Md. Arafat Rahman

Abstract

Assets failure is widely considered as one of the main causes of major accidents in chemical industries such as fires, explosions, and toxic gas releases. Asset criticality analysis is vital to prevent such accidents. This paper aims to model the asset criticality using traditional risk-based maintenance (RBM) and fuzzy RBM model. A case study has been performed on three main plants of TSP Complex Limited. Both models have been developed considering the factors like operational impact, operational flexibility, maintenance cost, safety, and environmental factor. Sulfuric acid (SA) plant has 2 critical assets, 10 semi-critical assets and 18 non-critical assets and has been found in semi-critical condition. On the other hand phosphoric acid (PA) plant and water treatment (WT) plant have been found as non-critical state as they have no critical assets. A fuzzy critical surface has been developed describing the transitional conditions from one criticality level to another criticality level. The proposed model can be used to prioritize the assets according to their critical value which enables to prepare the precedence list for taking action. This model is also applicable to other industries.

Conclusion

In this work, criticality modeling of process operations in chemical plants is proposed using risk-based maintenance (RBM) and fuzzy RBM approaches. The proposed model is strongly dependent on the real situation of the process operations. The case study has been performed in Chittagong TSP complex to check the proposed model. Assets of the three units of the plant were taken into consideration for the study of the model and Delphi method was used for the calculation of the information of each asset. Criticality level of each asset was selected from their corresponding risk value. The findings of the present study are summarized as follow: SA Plant: 2 critical assets, 10 semi-critical assets and 18 non-critical assets. It is in semi-critical state. PA Plant:No Critical asset, 4 Semi-critical assets and 21 Non-critical assets. It is in non-critical state. WT Plant:No Critical asset, 3 Semi-critical assets and 31 Non-critical assets. It is in non-critical state. The proposed model has been compared with the traditional RBM approach efficiently and results obtained good enough to converge with the traditional one. The deviations in the comparison can be minimized by varying the membership functions and fuzzy rules. This model can be an excellent tool for the management to take the maintenance strategy for the plant which will reduce risk of the entire plant.

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