Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

Iron ore sintering is a complex thermochemical process for bulk production of blast furnace raw material. The quality of sinter is very important for smooth operation and high productivity of the blast furnace since it improves the permeability.and reducibility of the burden material. A detailed mathematical model for the process, considering all the important phenomena such as gas-solid reaction, melting and solidification, flow through the porous bed, heat and mass transfer, etc., has been used for process simulation and analysis. Optimisation using a genetic algorithm has then been applied to evaluate the operating condition of suction pressure at different bed positions for optimum quality, productivity and thermal efficiency of the process. The genetic algorithm has several advantages over traditional optimisation techniques, since it can identify global optima under multimodal conditions, and do away with the need for gradient evaluation.

World University root node

Department of Chemical Engineering

Ironmaking and Steelmaking

Optimisation of suction pressure for iron ore sintering by genetic algorithm

Multiobjective Pareto optimal solutions for a semibatch isothermal epoxy polymerization process are obtained by adapting the binary-coded nondominated sorting genetic algorithm II (NSGA II). The number-average molecular weight and polydispersity index are taken as two objectives, where the first one is maximized and the second one is minimized. The decision variables are addition profiles of various reagents, e.g., the amount of addition for monomer, sodium hydroxide, and epichlorohydrin at different times, whereas the solution of all species balance equations is treated as a constraint. Because the number-average molecular weight and polydispersity index are sometimes not sufficient to describe the desired species growth, additional objectives such as the maximization of preferential formation of lower oligomers with the minimization of NaOH addition are also studied. For all practical purposes, semibatch-mode operations are preferred even if for the fulfillment of certain objectives batch-mode operations are theoretically competitive. A well-validated model taking care of all physicochemical aspects of a reaction mechanism is a prerequisite for this kind of study. Process simulation and optimization with close proximity to the available experimental conditions is definitely a distinguishable feature of this work that can direct the results toward actual plant realizations.

Industrial and Engineering Chemistry Research

Multiobjective optimization of a semibatch epoxy polymerization process using the elitist genetic algorithm

The elitist version of nondominated sorting genetic algorithm (NSGA II) has been adapted to optimize the industrial grinding operation of a lead-zinc ore beneficiation plant. Two objective functions have been identified in this study: (i) throughput of the grinding operation is maximized to maximize productivity and (ii) percent passing of one of the most important size fractions is maximized to ensure smooth flotation operation following the grinding circuit. Simultaneously, it is also ensured that the grinding product meets all other quality requirements, to ensure least possible disturbance in the following flotation circuit, by keeping two other size classes and percent solid of the grinding product and recirculation load of the grinding circuit within the user specified bounds (constraints). Three decision variables used in this study are the solid ore flowrate and two water flowrates at two sumps, primary and secondary, each of them present in each of the two stage classification units. Nondominating (equally competitive) optimal solutions (Pareto sets) have been found out due to conflicting requirements between the two objectives without violating any of the constraints considered for this problem. Constraints are handled using a technique based on tournament selection operator of genetic algorithm which makes the process get rid of arbitrary tuning requirement of penalty parameters appearing in the popular penalty function based approaches for handling constraints. One of the Pareto points, along with some more higher level information, can be used as set points for the previously mentioned two objectives for optimal control of the grinding circuit. Implementation of the proposed technology shows huge industrial benefits. © 2003 Elsevier Ltd. All rights reserved.

Chemical Engineering Science

Multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm

The epoxy-polymerization process can be better understood by investigating the underlying optimization problem involving a number of conflicting objectives and more than 20 decision parameters. A combination of minimization or maximization of objectives, such as the number average molecular weight, polydispersity index and reaction time, are considered in this paper. The first two objectives are related to the properties of a polymer, whereas the third objective is related to productivity of the polymerization process. The decision variables are addition quantities of various reactants, e.g. the amount of addition for bisphenol-A (a monomer), sodium hydroxide and epichlorohydrin at different time steps (modeled in a semi-batch operation), whereas the satisfaction of all species balance equations is treated as constraints. A multi-objective evolutionary algorithm (the elitist non-dominated sorting genetic algorithm or NSGA-II) is used to obtain a set of non-dominated solutions in a single simulation run. The results show a substantial improvement (with about 300% more productivity) over the benchmark condition (reported by performing a one-time addition of reactants in the beginning in a batch process). Importantly, this study brings out a salient aspect of using an evolutionary approach to multi-objective problem solving. The availability of multiple optimal trade-off solutions allows a process engineer to have salient information about the polymerization process. Changes in the distribution of various polymer species in the course of polymerization process as observed among various. Pareto-optimal solutions are identified and explained for this purpose. Such information provide important information about optimal operating conditions corresponding to different trade-offs among objectives, which are otherwise difficult to obtain. The systematic approach of starting from the two-objective problems to capture the essential features of interesting optimal operating conditions to finally solving the three-objective problem associated with the epoxy-polymerization problem in discovering the optimal trade-off interactions should motivate further such studies on other chemical process optimization problems. Overall, this paper demonstrates how fundamental optimization principles can be used systematically and reliably to find optimum operating conditions for complex chemical process operations. © 2004 Elsevier Ltd. All rights reserved.

Towards a better understanding of the epoxy-polymerization process using multi-objective evolutionary computation

Multiobjective Pareto optimal solutions for epoxy semi-batch polymerization process are obtained by adapting nondominated sorting genetic algorithm II (NSGA II). The objective is to produce polymer of maximum possible number average molecular weight (Mn) with a specified value of polydispersity index (PDI) and number average molecular weight in minimum possible time. The Mn and reaction time are, therefore, taken as two objectives where the first one is maximized and the second one is minimized. The decision variables are addition profiles of various reactants and the reaction time itself and PDI is treated as a constraint. In another optimization study, the time intervals are also been changed from hourly addition assumptions to equal interval additions for an optimized time frame. Additionally, similar analysis has been performed with a new addition strategy with total additions of reactants very close to available experimental conditions. Sensitivity analysis for estimated kinetic parameters and analysis for stabilization of products are also studied. A validated model, taking care of required physico-chemical aspects of the proposed reaction mechanism, is a prerequisite for this kind of study. © 2004 Elsevier Ltd. All rights reserved.

Computers and Chemical Engineering

Multiobjective dynamic optimization of a semi-batch epoxy polymerization process

An epoxy polymerization process is studied in semi-batch mode. Caustic has a very critical influence on epoxy polymerization process, which is modeled as a set of highly nonlinear coupled ODE's (ordinary differential equations). Owing to the highly complicated, nonlinear domain of analysis, "Differential Evolution (DE)" and "Genetic Algorithm (GA)" are used as optimization tools to identify the Mn-PDI Pareto set and study the best operating strategy with respect to NaOH addition. The moment of various oligomeric components during the semi-batch polymerization is also presented for a better understanding of the process. This study demonstrates the potential of evolutionary optimization algorithm to identify various operating philosophies of a semi-batch epoxy reactor for a targeted product quality.

Macromolecular Theory and Simulations

Effect of caustic addition in epoxy polymerization process: A single and multi-objective evolutionary approach

Continuous endeavors are going on in many research works to find out the strategy to mathematically model and optimize complex reaction networks in order to maximize the main product and at the same time keeping the reactor dimensions within some acceptable limits. The aim of this work is to provide with a strategy for efficient modeling and optimization of reaction networks for reaction controlled processes. Genetic algorithm (GA) has been used for optimizing complex search spaces with multiple optima. Formation of styrene monomer from the ethylbenzene dehydrogenation, with several by-products in a fixed bed reactor, is taken as an example for this study. Two activation energies are found to be the best in term of maximizing styrene productivity. © 2004 Elsevier B.V. All rights reserved.

Chemical Engineering Journal

Modeling of a reaction network and its optimization by genetic algorithm

Two layer sintering by charging green sinter mix with normal coke rate in the upper layer and reduced coke rate in the lower layer, can substantially reduce the coke rate and also improve the sinter quality by producing more uniform thermal profile through out the bed height. The two-layer sintering process have been analyzed by numerical simulation of the process by a detailed CFD based model, considering all the important phenomena like gas-solid reaction, melting and solidification, flow through porous bed, heat and mass transfer etc. Optimization techniques like genetic algorithm, differential evolution is then applied to evaluate the optimum coke rate in the two layers of the bed to produce the ideal thermal profile and melting fraction in the sinter bed for optimum sinter quality. Through this optimization method both high quality sinter with minimum return fines can be achieved along with reduced coke rate. Application of a genetic algorithm for this type of process optimization has several advantages over the traditional optimization techniques since it can identify global optimum condition and perform multi-objective optimization much easily for complex industrial process like iron ore sintering.

Materials Science and Technology 2003 Meeting

Optimization of Coke Rate and Sinter Quality by Genetic Algorithm for Two-layer Sintering of Iron Ore

The nondominated sorting genetic algorithm (NSGA) is adapted and used to obtain multiobjective Pareto optimal solutions for three grades of nylon 6 being produced in an industrial semibatch reactor. The total reaction time and the concentration of an undesirable cyclic dimer in the product are taken as two individual objectives for minimization, while simultaneously requiring the attainment of design values of the final monomer conversion and for the number-average chain length. Substantial improvements in the operation of the nylon 6 reactor are indicated by this study. The technique used is very general in nature and can be used for multiobjective optimization of other reactors. Good mathematical models accounting for all the physicochemical aspects operative in a reactor (and which have been preferably tested on industrial data) are a prerequisite for such optimization studies. © 1998 John Wiley & Sons, Inc.