Loss functions describe the economical consequences of the deviations from the target values. In recent years they have been used in wide range of application including process safety assessment. This paper provides a novel analysis to assess potential loss due to process deviation. The assessed losses help to better estimate process economic risk, which in turn assist in effective process system design and operational decision-making. The analysis is presented in four different development stages: (i) loss functions focusing on simple functions; (ii) loss functions with estimated maximum loss; (iii) loss functions focusing on probability distributions; and (iv) loss functions in which both distributions of variables and their dependencies are considered (i.e., hierarchical Bayesian based loss functions). Details discussion on development stages three and four are presented with case studies. First case study demonstrates application of inverted probability distribution and while second case study provides application of the hierarchical Bayesian loss functions. Advantage and disadvantages of different types of loss functions are also discussed. Finally, future research directions have been proposed.
hierarchical Bayesian loss function, inverted beta loss function, inverted normal loss functions, loss functions, Markov Chain Monte Carlo, decision making, Markov processes, risk assessment, risk perception, development stages, loss functions