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The quality of a treated effluent changes when there is a sudden variation in the influent flow to the wastewater treatment plant during dry, rain, and storm weather conditions. In this study, various influent flow conditions in an activated sludge process are considered that affect the sensitivity of effluent variables such as chemical oxygen demand (COD), biological oxygen demand (BOD), nitrate nitrogen (S NO ), ammonical nitrogen (S NH ), and total nitrogen (TN) with respect to varying internal recycle flow rate (Q a ), sludge recycle flow rate (Q r ), sludge wastage flow rate (Q w ) and oxygen transfer rate co-efficient of aerobic tanks (K L a (3,4,5) ). The analysis has been carried out based on benchmark simulation model no.1 (BSM 1) plant layout which comprises of two models namely activated sludge model no.1 (ASM 1) and simple one dimensional (Simple 1-D) Takacs model. Based on the present analysis, it is observed that the changes in influent flow rate have larger impact on the effluent variables. This variation can be subdued by introducing additional tanks to smoothen the perturbations or using internal recycle rate from the fifth tank in order to maintain the flow around the optimal influent flow rate. The sludge wastage rate has a greater impact on all effluent variables except nitrogenous variables during maximum flow conditions.

Complete separation can be achieved in selective homogeneous azeotropic mixtures by exploiting the pressure sensitive nature of the system. In the present work the optimal number of trays, feed location and reflux ratio for sequential column systems encountered in continuous pressure swing distillation (PSD) have been determined by use of two evolutionary techniques. Two industrially relevant systems: ethanol-water and acetonitrile-water have been considered. The Napthali-Sandholm model is solved to obtain the concentration and temperature profiles. The objective is to minimize the total cost using Genetic Algorithm (GA) and Differential Evolution (DE) for the two azeotropic systems. The techniques offer attractive features like applicability to discontinuous and non-differentiable search spaces.

Extracting natural gas and crude oil from deep wells requires a thorough understanding of multi phase flow phenomena. Designing of micro reactors employing different fluids requires similar understanding on a micro scale. Gas-Liquid two-phase flow mixing and separation commonly occur in such oil exploration and recovery on a macro scale and in case of micro fluid reactors in a micro scale. In this paper, two-phase flow mixing and separation is analysed for a horizontal channel with internal diameter 2.50 mm and a length of 202.5 mm. The numerical results obtained during two-phase flow mixing are validated with experimental results by employing image processing techniques. A numerical model is developed to study mixing and separation characteristics of slug flow regime. The separation efficiency of the T-junction partial separator for various superficial velocities, viscosity, surface tension and separation angles is analysed. The analysis will be useful for designing mini/micro reactors.

The main goal of this research is modeling and simulation of ethylene production through naphtha thermal cracking in a coil furnace reactor. The naphtha cracking and decoking processes are modeled based on the mass, energy and momentum balance equations considering a detail kinetic model at pseudo-dynamic condition. To develop a reliable and applicable model, a detail thermal model is considered to predict tube skin temperature. To prove accuracy of developed model and considered assumptions, the simulation results are compared with the plant data. In addition, the results of coke burning in the decoking cycle by mixture of air and steam are presented. It is appeared that ethylene production decreases during the process run time gradually. The simulation results proved that increasing feed temperature, feed pressure and combustion chamber temperature during the process run time is a practical solution to maintain production capacity at an acceptable level. Finally, the optimal operating condition of thermal cracking process is calculated during the process run time based on the time variant feeding policy to achieve maximum ethylene production capacity. It is appeared that applying optimal condition on the system could increase ethylene production capacity from 15.31 ton.h ‒1 to 15.49 ton.h ‒1 .

A recently proposed approach for the production of syngas is Tri-reforming process. Significant preferences of tri-reforming over conventional reformers are high methane conversion in a single unit, in situ heat generation, carbon dioxide consumption and adjustable outlet H 2 /CO ratio. In this work, a one dimensional mathematical model was developed for a novel axial-flow spherical packed bed tri-reformer. The results of reforming model were compared with the process data obtained from conventional Lurgi-steam reformer (CSR). Effects of feed flow rate, inlet temperature and length per radius (L/R) of the reactor on outlet hydrogen yield, methane conversion and H 2 /CO ratio were also studied. To maximize the outlet hydrogen yield, operation conditions and L/R ratio of the reactor were optimized applying differential evolution (DE) method. Obtained results were then compared with non-optimized condition. High methane conversion (99.68 %), high hydrogen yield (1.896) and much lower pressure drop were the superiorities of the presented reactor to the previous configurations.

A feed-forward neural network (FNN) and a layered recurrent neural network (LRNN) based two composition estimators, respectively, are designed for the purpose of tight product purity control for an ideal, quaternary, hypothetical, kinetically controlled, reactive distillation (RD) column. The output variables of the considered control structure i.e. the compositions, are estimated using the chosen tray temperatures as inputs to the estimators. The performances of the estimators in the control of the column for the servo, regulatory, feed impurity disturbances and catalyst deactivation are studied. The estimator based control is found to be effective for the on-spec product purity control. One-to-one relation between the number of tray temperature measurements and their sensitivity to the accuracy of estimation is observed. Overall, the performance of LRNN is found to be superior over the FNN for the throughput manipulations tested for the more number  of inputs to estimator.