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The main objective of the present investigation is to predict longitudinal dispersion coefficient (K x ) in natural streams using artificial neural network (ANN) technique based on most famous training functions such as Trainlm, Trainrp, Trainscg, Trainoss, and so on. To achieve the goal, hydraulic and geometric data (shear velocity, channel width, local flow depth, and mean longitudinal velocity) that are easily obtained in natural streams are used. First, we have tried to review the most well-known of published work in the field due to find out deficiencies of them. Second, new approach of ANN model based on the famous training functions is applied for predicting K x in natural streams and then the best architectures for each training functions is selected by trial and error. Finally, Levenberg-Marquardt training function (Trainlm) is selected as the best choice for training the network parameters. Determination coefficient (R 2 ) and mean absolute error for ANN (Trainrp) model were equal to 0.94 and 33 in the training and 0.95 and 30 in the testing steps, respectively. It is hoped that the presented methodology in the research, can be useful in river water quality management studies.

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Heat‐stable salts (HSS) can be generated by degradation of amines during absorptive CO2 capture. The removal of HSS from lean amine using electrodialysis was carried out with the aims of enhancing HSS removal efficiency and reducing energy consumption as high as possible using homogeneous and heterogeneous ion exchange membranes. The results demonstrate the significant effects of concentrated solution on removal efficiency, energy consumption, amine loss, and amount of effluent. In the case of using a lean amine of the gas refinery and water as a concentrated solution, the HSS removal can be enhanced by 30 % and the amount of effluent can be reduced by 40 % using a homogeneous membrane instead of a heterogeneous membrane. Furthermore, the energy consumption decreased by 51 %.

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