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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 In the present study, data-driven fault diagnosis (FD) systems of chemical plants dealing with frequent and rare faults are investigated. Although different faults occur with different frequencies in chemical plants, this issue has scarcely been addressed in developing a process FD system. A novel diagnostic framework based on the Bayesian network (BN) is proposed to incorporate fault frequencies. This probabilistic method can readily involve non-uniform probability distribution of faults and non-Gaussian probability distribution of features. The proposed approach includes not only a combination of tools but also information management. In fact, the imbalanced dataset, established by frequent and rare faults, promotes recursive updating of prior probabilities of faults. The performance of the BN was evaluated and compared with the conventional C4.5 method in the Tennessee-Eastman process benchmark. It was shown in this work that the diagnostic performance of the proposed approach versus the C4.5 method is more efficient. The importance of taking into account non-uniform probability distribution of faults for designing a FD system was highlighted. Furthermore, the effect of independent component analysis (ICA) of the imbalanced dataset on the FD was examined. The proposed framework versus the C4.5 method promises 37 % FD performance improvement for the dataset with a 10:1 imbalance index. [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]