A Multi-Objective Genetic Algorithm for determining efficient Risk-Based Inspection programs

• This paper proposes an original RBI multi-objective-based framework.
• The exhaustive evaluation of these feasible programs is impossible in practice.
• Thus, the effort to accomplish the analysis is fairly reduced.
• Tool to support efficient decisions related to mechanical integrity of equipment.

This paper proposes a coupling between Risk-Based Inspection (RBI) methodology and Multi-Objective Genetic Algorithm (MOGA) for defining efficient inspection programs in terms of inspection costs and risk level, which also comply with restrictions imposed by international standards and/or local government regulations. The proposed RBI+MOGA approach has the following advantages: (i) a user-defined risk target is not required; (ii) it is not necessary to estimate the consequences of failures; (iii) the inspection expenditures become more manageable, which allows assessing the impact of prevention investments on the risk level; (iv) the proposed framework directly provides, as part of the solution, the information on how the inspection budget should be efficiently spent. Then, genetic operators are tailored for solving this problem given the huge size of the search space. The ability of the proposed RBI+MOGA in providing efficient solutions is evaluated by means of two examples, one of them involving an oil and gas separator vessel subject to internal and external corrosion that cause thinning. The obtained results indicate that the proposed genetic operators significantly reduce the search space to be explored and RBI+MOGA is a valuable method to support decisions concerning the mechanical integrity of plant equipment.