Shabina Khanam

Selection of optimal feed flow sequence for a multiple effect evaporator system

Abstract A nonlinear model is developed for a SEFFFE system employed for concentrating weak black liquor in an Indian Kraft Paper Mill. The system incorporates different operating strategies such as condensate-, feed- and product-flashing, and steam- and feed-splitting. This model is capable of simulating a MEE system by accounting variations in τ, U, Qloss, physico-thermal properties of the liquor, F and operating strategies. The developed model is used to analyze six different F including backward as well as mixed flow sequences. For these F, the effects of variations of input parameters, T0 and F, on output parameters such as SC and SE have been studied to select the optimal F for the complete range of operating parameters. Thus, this model is used as a screening tool for the selection of an optimal F amongst the different F. An advantage of the present model is that a F is represented using an input Boolean matrix and to change the F this input matrix needs to be changed rather than modifying the complete set of model equations for each F. It is found that for the SEFFFE system, backward feed flow sequence is the best as far as SE is concerned.

Development of a new model for multiple effect evaporator system

Abstract A new simplified scalable mathematical model, based on concepts of stream analysis, temperature paths and internal heat exchange, has been developed for synthesis of a multiple effect evaporator systems. In this model, fresh feed is assumed to be composed of product and number of condensate streams, which come out from different effects and these are treated as separate streams. For the present work a septuple effect flat falling film evaporator system, used for concentrating black liquor in an Indian Kraft Pulp and Paper mill, has been considered. This system is being operated under backward sequence with condensate-, feed- and product-flashing as well as steam splitting in first two effects. The set of linear algebraic equations for this model are self-generated through programming and is solved simultaneously using Gaussian Elimination Method with partial pivoting. Results of the present approach are validated with published model and industrial data.

Design Modifications for Energy Conservation of Sponge Iron Plants

During the operation in the coal based sponge iron plant, a tremendous amount of heat is generated and significant part of this heat, associated with the waste gas, remains unutilized. It appears worth interesting to modify the process that facilitates the integration of heat available with the waste gas. In the present paper, for the utilization of heat of waste gas, two modifications, namely, case-1 and case-2, are proposed based on preheating of inlet streams. In case-1, preheating of feed material is considered, whereas in case-2, preheating of feed material as well as air is accounted. These cases are then compared with the existing waste heat recovery system of the plant based on coal consumption, operating and capital costs, profit, and payback period. It is found that both cases are better than the existing heat recovery system of the plant. However, case-2 is selected as the best heat recovery option. In comparison to the existing system, case-2 reduces coal and water consumption by 30.5% and 72.6%, respectively. Further, case-2 releases minimum waste gas to the atmosphere that makes the process environment friendly.

Optimization of the Amount of Chemical Dosage to Improve the Performance of Dissolved Air Flotation Unit

The present case study work is carried out on a typical steel industry in selection of the optimum amount of chemicals dosage used in DAF unit to remove oil and grease. Neural models are developed between DAF unit variables, i.e., the amount of chemical, the pH, and the amount of oil in the effluent. The amount of alum and polyelectrolyte are considered as independent whereas pH and amount of oil present in outlet stream are accounted as dependent variables. These input and output data are analyzed using ANN and GMDH method, which reduces the cost of the chemical/day-shift by 10.5% and 21.85%, respectively.

Simulation of Rotary Kiln Used in Sponge Iron Process Using ANN

In the present study, estimation of actual output parameters is carried out for a sponge iron production process by designing a Multilayer Perceptron model that uses a momentum learning algorithm. For this purpose data of temperature profile of rotary kiln are collected from typical sponge iron plant, which correlate four air inlets and twelve temperatures measured at different lengths of the kiln. Four different topologies are proposed for this data set to optimize the regression coefficients (R2). Firstly, these topologies are used to identify optimum value of output parameters based on value of R2. These values of output parameters meet the process requirements. Further, a better option is found to compute actual input parameters correspond to desired output. The analysis shows that to get desired output the input parameters are varied maximum by 38.9% in comparison to input parameters used in the industry.

A new approach for shell targeting of a heat exchanger network

Abstract A new approach is developed for targeting number of shells of a heat exchanger network, which directly accounts for the temperature cross. It is based on R and G values, where G is a dimensionless group defined to account the temperature cross. For this purpose design charts of FT(R,S) and FT(R,G) are developed and superimposed to create a single chart for each flow configuration. Here, R and S are two dimensionless groups called heat capacity flow rates and thermal effectiveness, respectively. In the present paper a chart for 1–2 shell and tube heat exchanger is shown. To show the reliability of this approach it is used to target number of shells for different heat exchanger network problems taken from open literature and the results are compared to that predicted from published methods. It is found that the present approach computes the number of shells with considerably less effort.

Selection of suitable model for different matrices of raw materials used in supercritical fluid extraction process

ABSTRACT Supercritical fluid extraction (SFE) process is the sustainable green process for the extraction. Mathematical modeling of SFE process is carried out using mass transfer resistances, which vary with the types of solute. In this paper, the effect of matrices such as leaves, flower concrete, flower bud, herb plant, shrub seed and vegetable matter is studied on extraction yield through different models. These models are solved using COMSOL Multiphysics 5.2 solver and results are validated with that of literature. Experimental data of each type of solute matrix are fitted in various models and best suited model is predicted.