Present paper is aimed to develop operation policy for a multi-purpose reservoir using Neuro-Fuzzy technique in an efficient way. Ramganga reservoir behind Ramganga dam, Kalagarh, India has been considered as a study reservoir. The developed policy minimizes the damage due to floods and droughts and determines optimum releases against demands for domestic supply, irrigation and hydropower generation for monsoon and non-monsoon periods. Three Fuzzy Rule Based (FRB) models for monsoon period and three for non monsoon period have been developed and tested. Actual releases have been used to formulate the general operation fuzzy rules. Releases computed from all developed models using Fuzzy Mamdani (FM) and ANFIS (Adaptive Neuro-Fuzzy Interactive System) -Grid and Cluster have been compared and it was found that ANFIS-cluster gives the best results but FM is more users friendly. For any expected inflow, reservoir level and demand, release can be calculated using developed GUI windows of the models.

Several studies have been conducted to improve the room temperature ductility of titanium aluminide intermetallics through alloy design and microstructure modifications. Ductility of two phase (D 2 +J) binary Ti aluminide intermetallics centered on Ti-48Al (at%) was reported as maximum (~1.5%) in desirable heat treatment condition and so more studies were attempted near to this composition. In the present work also, ductility has been studied for the alloy variants of this composition through fuzzy modeling. Neuro-fuzzy models were developed through Adaptive Neural Fuzzy Inference System (ANFIS) using subtractive cluster techniques. The input parameters were fuzzified with Gaussian membership functions to develop the fuzzy rules. The output of each rule was obtained by the evaluation of the membership values. Finally the overall fuzzy model response was obtained as the weighted average of the individual rule response. Ductility database were prepared and three parameters viz. alloy type, grain size and heat treatment cycle were selected for modeling. Additional, ductility data were generated from literature based experimental data for training and validation of models on the basis of linearity and considering the primary effect of these three parameters. Adequacy of developed models was evaluated with the generated data sets. Different evaluation measures were considered and the resulting graphs from the developed model were analyzed. The results of the fuzzy models were found to be very close to the literature based generated data and it also showed the possibility of improving ductility upto 7% for multicomponent alloy with grain size of 10-50Pm following a multistep heat treatment cycle.

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Journal of Advanced Chemical EngineeringJ o ur nal of A d v a n ce d Che m ic a l E ng ineer in g AbstractA two-layer Artificial Neural Network (ANN) model was engendered to soothsay the deliberation efficacy of Fe (II) particles from fluid arrangement utilizing chitosan magnetite nano composites (CMNs). The sorbet stock arrangement was yare by dissolving a pre-computed amount of FeCl 3 in twofold refined water to give last fixation 100 mgl −1 . The stock arrangement was debilitated to get standard arrangements with fixation in the scope of 5-30 mgl −1 and their last pH was transmuted in accordance with 4.5. Fifty millilitres of FeCl 3 arrangement of fancied focus was put in a 125 ml Erlenmeyer flagon containing 0.02 g of CMN sorbent. A period of 3 hours was discovered adequate to accomplish the balance. The ANN model was intended to suspect sorption efficacy of CMNs for target metal particle by amalgamating back spread (BP) with guideline segment examination. A sigmoid axon was utilized as exchange capacity for information and yield layer. The Levenberg-Marquardt calculation (LMA) was connected, giving a base estimation of mean squared mistake (MSE) for preparing and cross approbation at the 6th place of decimal.

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