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This work presents a judicious screening of 986 possible ionic liquid (IL) combinations for the removal of Endosulfan using COSMO-RS (Conductor-like Screening Model for Real Solvents) model. Initially, benchmarking studies have been carried out for α-Endosulfan, β-Endosulfan, Endosulfan sulfate, Endosulfan-alcohol, Endosulfan lactone, and Endosulfan ether by comparing COSMO-RS experimental and predicted octanol–water partition coefficients. Thereafter, COSMO-RS selectivity predictions were done on 986 ionic liquid combinations at infinite dilution. The order of selectivity for the five cation groups were found to be as follows: [TBP] > [TIBMP] > [TBMP] > [C 2 DMIM] > [BEPYR] > [DPPYR] > [C 4 DMIM] > [C 8 MPY] > [BTNH] > [BETNH]. Highest selectivity was obtained for phosphonium based IL namely: [TBP][TOS] (212.5). Anions such as [C 8 H 17 SO 4 ], [Br], [Sal], [TOS], [MDEGSO 4 ], and [DEC] contributed high selectivities because of the absence of sterical shielding effect around their charge centers. Further capacity and the performance index (PI) values were calculated and predicted along with selectivity. The increasing order of performance index values were found to follow: [TBP][Sal] (1.71+E5) > [DPPYR][Br] (1.07+E6) > [C 2 DMIM] (1.01+E6) > [C 8 MPY][Cl] (1.6+E5) > [BETNH][DEC] (1.2+E5).

This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

This paper discusses the development of a one-dimensional mathematical model to describe the heat and mass transfer during the hot-compression of medium density fibreboard (MDF) composite panels. Five primary variables were considered during the model development: air content, water vapour content, bound water content, and temperature within the mat, and the extent of the cure of the adhesive system, characterized by the cure index. Different heat and mass transfer processes were identified for the transport of the heat and the moisture phases. The heat was transported by conduction and convection due to a temperature gradient, and the exothermic energy released by the curing of the urea formaldehyde resin, while the water phases were transported by bulk flow and diffusion due to total pressure and concentration gradients. The resulting differential-algebraic equation system was solved by the finite difference method. The mathematical model predicted temperature, moisture content, partial vapour pressures, and extent of adhesive cure within the mat structure under a typical hot-compression process. The model results allow a better understanding of the interacting mechanisms involved in a complex production process. The model can also assist to optimize the hot-pressing parameters for improved quality of MDF panel products, while reducing pressing time.

Consuming a high amount of energy in industrial dryer makes the drying as an important and highly energy-intensive industrial process. In this study, the required energy for drying of apple slice was supplied from the outlet heat of an engine generation set exhaust. The drying behavior of apple slice was studied in a combined heat and power (CHP) dryer system at four engine load levels (25, 50, 75, and 100%) in order to provide different temperatures (50, 65, 80, and 95°C), and at three levels of drying product thickness (3, 5, and 7 mm) with the constant air flow velocity of 1 m/s. The empirical data from experiments with variants of semi-theoretical and empirical models were evaluated and finally, a suitable model proposed by Midilli et al. was selected to be the best model, as for as the RMSE, R 2 , and χ 2 criterion is concerned. Comparing the results from implementing artificial neural network (ANN) and mathematical models, it was found that the dynamic ANNs is more powerful for modeling the drying process of apple slice in a CHP dryer system than static ANNs and mathematical models.

To get the better product quality and to decrease the energy consumption of the distillation column, an accurate and suitable nonlinear model is crucial important. In this work, two types of model have been developed for an existing experimental setup of continuous binary distillation column (BDC). First model is a theoretical tray-to-tray binary distillation model for describing the steady-state behavior of composition in response to changes in reflux flows and in reboiler duty. Another model is an artificial neural network (ANN)–based input/output data relationship model. In ANN-based model, temperature of first tray, feed flow rate, and column pressures have been taken in addition to reflux flow rate and reboiler heat duty as inputs to give the more accurate I/O relationship. The comparison of output of ANN model and the equation-based model with the real-time output of the experimental setup of BDC has been given for the validation of developed models.

This study proposed a new model based on the solvation phenomena. The ionic activity coefficients of individual ions of some 1:1 and 1:2 salts were correlated successfully. Comparison was made between the results of the present model and those of Khoshkbarchi–Vera (KV), Pitzer, and Lin–Lee models. The average relative errors for cation and anion were 3.03, 3.53, 3.45 and 1.24, 1.14, 1.38, respectively, in Lin–Lee, KV, Pitzer models, whereas they are 1.81 and 0.66 in the proposed model.These results show that the proposed model improved the correlation of the individual ionic activity coefficients. Contrary to arguments found in the literature, it is also demonstrated that the short-range solvation contribution is a linear function of ionic strength.