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Traditional criteria for designing processes that utilize only economic aspects can have a negative impact on the environment and society. In this study, a process for the production of dimethyl ether (DME) from methanol is evaluated by employing sustainability metrics. Operational conditions are optimized by implementing a rigorous global multi-objective optimization algorithm based on maximization of economic performance measured by the return on investment (ROI) and minimization of environmental and social impacts. The most efficient operational conditions for DME production based on sustainability criteria are obtained with a homotopy continuation method in conjunction with a process simulator. The resulting conditions indicate that the global warming metric of the DME process is decreased more than 10 times and the decrease of photochemical smog formation, mass intensity and energy intensity is 96%, 12% and 9%, respectively. The optimized, sustainable process shows an only an insignificant reduction in terms of economic aspect. [ABSTRACT FROM AUTHOR]

Abstract: The objective of the research presented in this paper is to investigate the application of a new metaheuristic algorithm called ant colony algorithm to petroleum engineering problems. This algorithm is usually used for discrete domains, but with some modifications, has been applied to continuous optimization. In this paper, three examples with continuous and discrete parameters as well as known solutions and varying degrees of complexities are presented as an illustration for solving a large class of process optimization problems in petroleum engineering. Results of case studies show the ability of ant colony algorithm to provide fast and accurate solutions. [ABSTRACT FROM AUTHOR]

Present study addresses a fault diagnosis system based on micro-macro data for monitoring chemical plants. Macro data are encapsulation of process history in terms of prior probability distribution of faults which is achieved using the fault tree analysis. Then, the fault diagnosis system is developed based on an imbalanced dataset, established by frequent and rare faults. In addition, micro data, records of sensors at each time step, are used to predict faults. The Bayesian network is proposed to integrate micro-macro data for diagnostic purposes. Efficiency of the proposed framework was evaluated for an industrial gas sweetening unit. It was shown that the diagnostic performance of the proposed approach is remarkable. Thus, it was concluded that fusion of micro-macro data enhances the performance of the fault diagnosis system. Furthermore, extraction of significant features using the principal components analysis promotes the diagnosis performance. The proposed framework, compared to conventional ones, shows 21% improvement in terms of accuracy. In addition, error bands of fault prediction decreased through implementing a hierarchical strategy. [ABSTRACT FROM AUTHOR]

A hybrid optimization technique, GA–SQP, is developed in which the genetic algorithm (GA) which is a stochastic method is combined with the sequential quadratic programming (SQP) method which is a deterministic method. This method was used to determine the kinetic parameters of the set of highly nonlinear hydrogenation reactions. Catalyst deactivation was also taken into account. The ability of GA and SQP in solving this type of problem was investigated. It was shown that although the SQP is fast, it is not able to solve this problem properly and is very sensitive to the choice of initial point. The GA was able to solve the problem after a large number of generations. It was shown that the new GA–SQP hybrid method is able to determine the final solution considerably faster than the GA while it is not sensitive to the initial point. [Copyright &y& Elsevier]