Process Systems Engineering

Process Systems Engineering (PSE) focuses on a complete, life cycle view of the manufacturing process in chemical engineering, beginning from the scale of molecule discovery &  scale up to the other end of spectrum relating to achieving manufacturing excellence and minimizing environmental impact. The PSE research has been focusing on these various individual steps in the life cycle of process engineering from both theoretical as well application perspectives. Beginning at the smallest scale of molecular modeling, research work at the  department has focused on Novel multi-scale simulation techniques for simulating complex interacting systems.The molecular scale information is employed with macroscopic models to describe chemical processes at the device length scales. Attempts to exploit the predictive capabilities of these multi-scale models for optimizing aforementioned devices are also currently underway. At the larger scale, the group has been focusing on the development of a generalized reactor model framework that can accommodate the wide diversity of chemical reactors. Establishing empirical cause and effect relationships for the purposes of process development, scale-up, process optimization, advanced process control, as well as fault detection and diagnosis, has been an area of significant activity in the systems engineering group. Basic and advanced optimization has been a focus area of research in the  department with several important and critical applications. Optimization for sensor network design that balances different criteria, such as process observability, precision & accuracy of parameter estimates and fault isolability, and overall cost of the sensor network has been an another active research area of the group. The group also focuses on the design of energy efficient heat exchanger networks along with approaches to identify opportunities for process intensification, i.e evolving substantially smaller, cleaner, and more energy-efficient designs. Some of key applications that are being considered are design of novel reactive separations methods for important industrial systems and design of new and alternate process routes  related to green manufacturing. Basic and advanced process control approaches are deployed in chemical process manufacturing to realize the optimal targets resulting from design and/or operational optimization steps. Model predictive control (MPC) has been one of the popular model based control algorithms. The group works on multi-parametric MPC approach with special  applications to fast transient systems. Biological systems exhibit several interesting phenomena at the cell level such as significantly amplified sensitivity of enzyme cascades. To develop a better understanding of these interesting phenomena, control theoretic approaches have been successfully used to represent and explain the feedback-like structures at the cell level.

Sub Research areas

Development of a Decision Support System for Management of Emergency Operations

Emergency management in the event of an occurrence of an acute accident situation requires the invocation of multiple systems and recources both onsite and offsite of a chemical process facility. During such emergency handling operation significant decisions are required to be made in a timely and efficient manner so as to effect rapid assembly of people, evacuation, and the technical management of the emergency so as to reduce human and economic losses and a protracted loss of production, and finally a quick turnaround.

Development of Safety Regulations: Integration of Cost-Benefit Analysis

Risk is integral to modern society. As a society evolves, there is an enhancement in societal perception of risk and in the need for mitigating
them. This translates into enactment of legislation to control such risks.
However, such legislative efforts do not integrate costs and benefits associated

Development of a risk-informed decision framework to derive the optimal organizational safety budget across globally dispersed manufacturing sites

Major chemical accidents not only result in immediate human and property losses, but can lead to significant losses in way of business interruptions which have the potential for affecting the viability of an organization. As part of overall process safety management, one of the critical strategies against such debilitating losses is anticipatory investment into safety practices that can help avoid future accident costs.

Development of decision-support system for enabling socially acceptable approaches to hazardous process plant siting

The tragic accident involving the leak of methyl isocyanate in Bhopal in 1984, which to date has been the largest industrial disaster, has led to formulation of a large number of health and safety regulations worldwide, one of them being application of land-use planning around major hazard facilities so as to control the risk to public, while at the same time balance it with prospective economic development that may ensue from industrial expansion.

Studies on assessment of lacuna in Indian industrial risk governance framework, and development of strategies to rectify them

The concept of risk governance encompasses critical decision-making for risk management, communication and monitoring of the outcome of risk strategies. It necessarily involves the engagement of all concerned stakeholders in the governance process: the government, the institutions, industry, non-governmental agencies and the general public.

Optimal operation of energy integrated batch distillation

Distillation is one of the most commonly used as well as the most significant contributor of energy in chemical processing complex. Energy integration can improve the sustainability of the process by reducing utility requirement in batch distillation. However, operation of such columns is challenging. In the light of this, this project aims as developing optimal operating policies and associated model-based control strategies for such distillation columns. 

Skills required: Matlab for simulations, basic knowledge of optimisation and advanced control