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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]

It is a common procedure to save energy in industrial distillation towers by preheating and precooling the feed with heat transfer from the bottom and top products. An example is oil sweetening by distillation. It is shown here using simulation that if, before entering the unit, the oil feed is split into two streams and only one of them is preheated, further savings of energy (up to 50%) can be achieved. The same results are achieved by splitting the feed to an ethane-ethylene distillation column and precooling only one part of it just before entering the tower. In this study, the effects of feed splitting into hot and cold towers were investigated in order to reduce the duties of the reboiler or condenser and the total annual cost (TAC). Also, consideration was given to obtaining a common design in order to achieve greater flexibility for the required operating schemes. The investigation was carried out as an optimization problem defined by effective parameters affecting the reboiler and condenser duty and product recovery. Models for finding the best feed split-fraction ratio, optimal locations for the feeds, number of trays required for a specified separation, and best operating pressure for a distillation column with feed splitting are presented. When a homotopy continuation method was used in MATLAB and it was connected to the ASPEN HYSYS simulator, the optimal values for decision variables were calculated. It is observed that the optimal feed-splitting technique leads to a 49.2% reduction in energy demand in an oil sweetening process and a 24% reduction in reboiler duty and a 22% reduction in condenser duty in an ethane-ethylene separation process in comparison with conventional processes. [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]

Organic-rich deposits of Qalikuh locality, Zagros Basin, Southwest of Iran are unconventional hydrocarbon resources considered as an oil shale, which is hosted within a carbonate succession with the age range of Middle Jurassic to Lower Cretaceous. Leco carbon, Rock–Eval VI and modified Fischer Assay analysis were performed on some outcrop samples of these resources to investigate the occurrence of oil shale. In the studied samples, mean total organic carbon (TOC) is 17.3 wt.% and the average of petroleum genetic potential (S 1 + S 2 ) is 98 kg HC/t rock. Furthermore, TOC has direct relationship with petroleum genetic potential. Hydrogen index (HI)–T max diagram indicates that the organic matter corresponds to Type II kerogen of probable marine origin with high quality of oil generation. According to Rock–Eval data, and bitumen reflectance (BR r ), the samples are thermally early mature. Based on the studied samples, oil yield of the Qalikuh oil shales ranges from 6.1 to 7.9 wt.%. Mean oil–gas ratio is 2.7 and mean oil yield is 56.7 l/t rock. According to the observations, the production value of Qalikuh oil shale is more or less similar in the entire studied regions. Whereas the total production is below the expected values, the produced oil is considerably lighter (higher API) than that of most other known oil shales. [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]