Kailash Singh

A review on reactive adsorption for potential environmental applications

The aim of this paper is to present a critical review on reactive adsorption processes. The impact of surface modification on adsorption behavior of various adsorbents in context of reactive adsorption has been reviewed. Various characterization and detection methods involved to access and verify the surface morphology of adsorbent, presence of surface functionalities on adsorbent, and concentration of adsorbate have been concisely presented. The paper also delves into the inadequately researched grey areas of reactive adsorption which require further attention such as modeling and adsorbent regeneration so as to make the process more economic. The applicability of reactive adsorption to ensure a cleaner environment has also been briefly discussed. This article also underlines the areas, in which reactive adsorption can be implemented on a pilot scale.

Neural network applications for detecting process faults in packed towers

Abstract Artificial neural networks can be used as a fault diagnostic tool in chemical process industries. Connection strengths representing correlation between inputs (sensor measurements) and outputs (faults) are made to learn by the network using the back propagation algorithm. Results are presented for diagnostic faults in an ammonia–water packed distillation column. First, a 6-4-6 network architecture (six input nodes corresponding to the state variables and six output nodes corresponding to the six malfunctions) was chosen based on the minimum root-mean-square-error and mean absolute percentage error; and a maximum value of the Pearson correlation coefficient (CP). The values of the learning rate, momentum and the gain terms were taken as 0.8, 0.8 and 1.0, respectively. The detection of the designated faults by the network was good. Relative importance of the various input variables on the output variables was calculated based on the partitioning of connection weights which showed that bottoms temperature, overhead composition and overhead temperature are not much affected by the disturbances in feed rate, feed composition and vapor rate in the given range. This resulted in a simplified 3-4-6 net architecture with similar capabilities as the 6-4-6 net thereby reducing the number of computations.

Control of Reactive Distillation Column: A Review

The objective of this paper is to give a critical survey of the present status within the field of control of a reactive distillation column. Control of a reactive distillation column is a challenging task due to process nonlinearity and complex interactions between the vapor-liquid equilibrium and chemical reactions. There are different types of control methodologies, which have been studied in the reactive distillation, ranging from a simple proportional-integral (PI) controller to advanced model predictive controllers (MPC) such as dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC), robust multivariable predictive control technology (RMPCT), generalized predictive control (GPC), and other advanced control techniques. With the goals of optimal performance, energy conservation and cost effectiveness of process operations in industries, the design of optimal controllers and controller performance assessment have received great attention in both industries and academia. The main objective of control is to maintain the product purity within the desired range.

Prediction of Biodiesel Properties from Fatty Acid Composition using Linear Regression and ANN Techniques

Abstract Biodiesel is currently the most widely accepted alternative fuel for diesel engines due to its various advantages. The fatty acid composition of vegetable oils affects the fuel properties of biodiesel, such as viscosity, flash point, fire point, cloud point, pour point, iodine value and saponification value. In the present work, biodiesel was prepared from different vegetable oils and its physical properties measured. Artificial neural networks (ANNs) with 6-4-1 and 6-5-1 architecture were used to determine the relative importance of the fatty acid (palmitic acid, stearic acid, oleic acid, linoleic acid, linolenic acid and erucic acid) composition of vegetable oils affecting biodiesel properties. The results show that the ANNs precisely predict the properties of biodiesel derived from certain vegetable oils better than a linear regression model. The relative importance of the input variables shows quantitatively the effect on biodiesel properties.

Modeling and model predictive control of dividing wall column for separation of Benzene–Toluene-o-Xylene

In this paper, dividing wall column (DWC) has been chosen for a BTX (Benzene–Toluene–o-Xylene) system. A MATLAB® program has been written for nonlinear unsteady-state DWC, which is used in Simulink environment for control of the system by Model Predictive Control (MPC). Compositions of the three products (benzene, toluene, and o-xylene) are indirectly controlled by controlling the corresponding temperatures of the respective tray due to requirement of online analyzer. The temperature of uppermost tray in the rectifying section, stage temperature in the main column corresponding to the side stream withdrawn, and the bottom stage temperature in the stripping section have been chosen in order to maintain the compositions of the three products. The manipulated variables are reflux rate (L0), side-stream flow rate (SSRF), and reboiler heat duty (QB). It has been observed that MPC shows good performance even in the presence of ±10% change in the feed flow rate, feed composition, and liquid split factor in comparison with conventional controllers. The MPC has less settling time (almost 1.5 h) compared with the PI controller (approximately 3–4 h).

Dynamic Simulation of Reactive Batch Distillation Column for Ethyl Acetate Synthesis

A detailed mathematical dynamic model of reactive batch distillation column is formulated for ethyl acetate synthesis and presented in terms of differential and algebraic equations (DAEs). These DAEs are solved using fourth order Runge-Kutta method in MATLAB® to obtain the detailed column dynamics. The simulation results provide the dynamics of reboiler and distillate compositions, reboiler temperature, ethyl acetate purity in the accumulated distillate and conversion of the reactants. These results are analyzed to derive the optimum operating policy, i.e., reflux ratio and batch time.

Neural network and support vector machine predictive control of tert-amyl methyl ether reactive distillation column

An algorithm of model predictive control based on artificial neural network and least-square support vector machine method is presented for a class of industrial process with strong nonlinearity such as tert-amyl methyl ether (TAME). Integral constant is added to improve the performance of the controller. In the present work, two different control methodologies neural network predictive control (NNPC) and support vector machine-based predictive control (SVMPC) are implemented and compared with a conventional proportional-integral-derivative (PID) control methodology to a TAME reactive distillation column. The simulation result shows that both NNPC and SVMPC gives better control performance than PID for set-point change as well as for load change of±10% in methanol feed flow rate and molar ratio of methanol to isoamylene in reactor effluent feed.

Simulation and sensitivity analysis for biodiesel production in a reactive distillation column

The conventional process for biodiesel production by transesterification is still expensive due to a need of high excess of alcohol required and its recovery by distillation. The use of a reactive distillation process can reduce the amount of alcohol in the feed stream as it works on a simultaneous reaction and separation. In the present study, a mathematical model has been developed for biodiesel production from triglycerides in a reactive distillation column, which has been validated with the reported data and CHEMCAD results. The effects of process parameters such as methanol to oil feed ratio, feed temperature, and reaction time have been investigated. The sensitivity analysis shows that yield of ester increases with methanol to oil ratio and number of stages, however, it decreases with fl ow rate. The MATLAB simulated results show that methanol to oil molar ratio of 5:1 produces 90% (by wt.) of methyl ester in a residence time of 4.7 minutes.

Parametric studies and simulation of PSA process for oxygen production from air

Parametric studies and simulation of PSA process for oxygen production from air A numerical simulation and parametric studies for the separation of air using 5A zeolite for the production of oxygen are presented for a basic two bed pressure swing adsorption (PSA) process. The simulation is based on an in-house program ‘PSASOL’ developed in MATLABR. The transient process of PSA has been described by a set of partial differential equations, which were solved using a finite difference method. Simulation results have been validated with the experimental data from literature. Based on the simulation results, an optimal set of operational parameter values has been obtained for the PSA bed. The values of the optimal parameters, viz. adsorption pressure, cycle time, feed rate, and product rate have been found to be 2.5 atm, 150 s, 15 cm3/s, and 2.55 cm3/s, respectively. For the optimal conditions, purity of 95.45% and recovery of 77.3% have been achieved. It has also been found that a longer tubular unit with the length to diameter (L/D) ratio of 10.5 is advantageous. The estimated pressure drop across the bed has been found to be negligible. Power consumption and productivity have also been computed.

A review on membrane applications and transport mechanisms in vacuum membrane distillation

Abstract The main aim of this article is to provide a state-of-the-art review of the experimental studies on vacuum membrane distillation (VMD) process. An introduction to the history of VMD is carried out along with the other membrane distillation configurations. Recent developments in process, characterization of membrane, module design, transport phenomena, and effect of operating parameters on permeate flux are discussed for VMD in detail. Several heat and mass transfer correlations obtained by various researchers for different VMD modules have been discussed. The impact of membrane fouling with its control in VMD is discussed in detail. In this paper, temperature polarization coefficient and concentration polarization coefficient are elaborated in detail. Integration of VMD with other membrane separation processes/industrial processes have been explained to improve the performance of the system and make it more energy efficient. A critical evaluation of the VMD literature is incorporated throughout this review.