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An attempt has been made in the present study to develop generalized slurry flow model using CFD and utilize the model to predict concentration profile. The purpose of CFD model is to gain better insight into the solid liquid slurry flow in pipelines. Initially a three-dimensional model problem was developed to understand the influence of the particle drag coefficient on solid concentration profile. The preliminary simulations highlighted the need for the correct modelling of the inter phase drag force. The various drag correlations available in literature was incorporated in a two-fluid model (Euler-Euler) along with the standard k-? turbulence model with mixture properties to simulate the turbulent solid-liquid flow in a pipeline. The computational model was mapped on to a commercial CFD solver FLUENT6.2 (of Fluent Inc., USA). To push the envelope of applicability of simulation, the recent data of Kaushal (2005) (with solid concentration up to 50%) was selected to validate the three dimensional simulations. The experimental data consists of water-glass bead slurry at 125& 440 micron particle with different flow velocity (from 1 to 5 m/s) and overall concentration up to 10 to 50% by volume. The predicted pressure drop and concentration profile was validated by experimental data and shows excellent agreement. Interesting findings were come out from the parametric study of velocity and concentration profiles. The computational model and results discussed in this work would be useful for extending the applications of CFD models for simulating large slurry pipelines.

This paper describes a robust hybrid artificial neural network (ANN) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta-parameters. The algorithm has been applied for prediction of critical velocity of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters. 

Fish, being a good source of nutrients, is often cooked by different methods before consumption, which affect the beneficial quality detrimentally. In this study, Catla catla, and mustard oil are selected as representative of fish and cooking oil for frying, respectively, because of their agricultural importance and worldwide demand. Extensive experiments are performed varying the effective processing variables of conventional frying viz., temperature (140 °C‐240 °C), time (5 min–20 min) and oil amount (25 ml/kg of fish‐100 ml/kg of fish) to correlate the drastic reduction of the nutritional quality indices, that is, ω‐3/ω‐6 and cis/trans‐fatty acids (FAs) profiles of fish after frying. To establish a nonlinear correlation between these inputs and outputs, an exhaustive search of all available artificial neural network (ANN) algorithms and activation functions is executed for the development of a model. The hybrid robust process approach integrating ANN with differential evolution (DE) and simulated annealing (SA) are employed to optimize the cooking parameters for regaining nutritional impact. After frying ω‐3/ω‐6 and cis/trans‐FAs ratio deteriorated by 76.65% and 92.68%, respectively, than the fresh samples. The ANN‐DE and ANN‐SA formalism efficiently enhanced these nutritional parameters up to 33.18% and 79%, respectively. Practical applications The present study applied artificial neural network (ANN) as an advanced alternative modeling tool to propose a generalized nonlinear correlation between temperature, time, oil amount, and nutritional values, that is, ω‐3/ω‐6 and cis/trans‐fatty acids (FAs) profiles of fried fish. Frying time provided a strong impact on food nutrition compared to other two input variables. Frying process detrimentally affected both the nutritional indices, that is, ω‐3/ω‐6 and cis/trans‐FAs profiles. The meta‐heuristic, stochastic optimization algorithms, namely differential evolution and simulated annealing along with ANN‐based processed model were implemented successfully to tune the cooking parameters, so that food quality indices of fish improved again to maximum value. The artificial intelligence modeling, along with optimizing methodology based parameters tuning approach described here is generic and can be advantageously extended to other experimentation of food process engineering. Besides, the finding of this study will benefit common people also.

This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.

A pilot scale Upflow Anaerobic Contactor (UAC), based on upflow sludge blanket principle, was designed to treat vinasse waste obtained from beet molasses fermentation. An assessment of the anaerobic digestion of vinasse was carried out for the production of biogas as a source of energy. Average Organic loading rate (OLR) was around 7.5 gCOD/m3/day in steady state, increasing upto 8.1 gCOD/m3/day. The anaerobic digestion was conducted at mesophilic (30–37 °C) temperature and a stable operating condition was achieved after 81 days with average production of 65 % methane which corresponded to a maximum biogas production of 85 l/day. The optimal performance of UAC was obtained at 87 % COD removal, which corresponded to a hydraulic retention time of 16.67 days. The biogas production increased gradually with OLR, corresponding to a maximum 6.54 gCOD/m3/day (7.4 % increase from initial target). A coupled Artificial Neural Network-Differential Evolution (ANN-DE) methodology was formulated to predict chemical oxygen demand (COD), total suspended solids (TSS) and volatile fatty acids (VFA) of the effluent along with the biogas production. The method incorporated a DE approach for the efficient tuning of ANN meta-parameters such as number of nodes in hidden layer, input and output activation function and learning rate. The model prediction indicated that it can learn the nonlinear complex relationship between the parameters and able to predict the output of the contactor with reasonable accuracy. The utilization of the coupled ANN-DE model provided significant improvement to the study and helps to study the parametric effect of influential parameters on the reactor output.

Owing to the wide utilization of heat exchangers in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until given heat duty and set of geometric and operational constraints are satisfied.Although well proven, this kind of approach is time consuming and may not lead to cost effective design. The present study explores the use of non-traditional optimization technique: calledParticle swarm optimization (PSO), for design optimization of shell and tube heat exchangers from economic point of view. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tubelength, bafflespacing, number of tube passes, tubelayout, type of head, baffle cutetc and minimization of total annual cost is considered as design target. The presented PSO technique is conceptually simple, has only a few parameters and is easy to implement.Furthermore, the PSO algorithm explores the good quality solutions quickly, giving the designer more degrees of freedom in the final choice with respect to traditional methods. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm . The PSO method leads to a design of a heat exchanger with a reduced cost of heat exchanger as compare to cost obtained by previously reported GA approach.