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In 2016, we introduced the concept of model-predictive safety (MPS; Ahooyi et al, AIChE J. 2016; 62:2024-2042. MPS is a proposed innovation in functional safety systems to methodically account for process nonlinearities and variable interactions to enable predictive, prescriptive actions, while existing functional safety systems generally react when individual process variables exceed thresholds. MPS systematically utilizes a dynamic process model to detect imminent and potential future operation hazards in real time and to take optimal preventive and mitigative actions proactively. This work expands the concept of MPS and formulates two min-max optimization problems, offline solutions of which are the optimal proactive preventive and mitigating actions that MPS takes online, in response to predicted process operation hazards. A nested particle-swarm optimization (PSO) algorithm is proposed to solve the min-max optimization problems. The application and performance of the min-max optimization formulations, the PSO algorithm, and MPS, applied to two chemical process examples, are shown through numerical simulations. K E Y W O R D Schemical processes, model-predictive safety, predictive alarm, process constraints, process safety, receding horizon

Community detection decomposes large‐scale, complex networks “optimally” into sets of smaller sub‐networks. It finds sub‐networks that have the least inter‐connections and the most intra‐connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi‐objective optimization problem. The whale optimization algorithm is extended to enable it to handle multi‐objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population's positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non‐dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process and several benchmark community‐detection problems.

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