Pavan Kumar Naraharisetti

From PSE to PSE2-Decision support for resilient enterprises

Abstract In recent years, the process systems engineering (PSE) community has recognized the need to address chemical enterprises comprising globally distributed, but strongly interacting, facilities. We examine this extension of PSE, which we call the PSE of enterprise (PSE2), as it relates to the five traditional PSE areas of system representation, modeling and simulation, synthesis and design, planning and scheduling, and control and supervision. We illustrate the strong structural, operational, and methodological parallels between PSE and PSE2 in this study.

Capacity Management in the Chemical Supply Chain

Abstract Long term capacity planning involves locating and allocating the production capacities and inventory capacities, and short term capacity planning involves the production allocation and distribution of materials. Most existing literature deals with them individually and also consider only investments in capacities and do not consider disinvestment. Combining the above problems into one would give a holistic picture of the entire supply chain and allows in better planning, leading to greater efficiency in terms of reduced costs or increased profit. In this work, we present a new deterministic capacity management model, which considers facility location, (dis)investment in capacity, production allocation, distribution of materials and regulatory factors among others. Also, we have improved the formulation of existing literature by using fewer binary variables.

Optimal supply chain redesign using genetic algorithm

Supply chain redesign involves decisions regarding the timing, amount and location attributes of the investment and disinvestment in facilities, production, purchase of raw materials, sale of products, loans and bonds for raising capital, signing of contracts for material purchase and sales, such that the profit is maximized. In this work, we use genetic algorithm to obtain the supply chain redesign plan while maximizing the profit. Genetic algorithms (GA) are best suited for unconstrained problems and we present a novel formulation of the supply chain redesign problem in an unconstrained fashion. To demonstrate this new and unconstrained formulation, we solve the problem which we previously presented (Naraharisetti et al., 2006), where we developed a novel MILP model for supply chain redesign and solved it using Cplex.

Factors Influencing CO2 and Energy Penalties of CO2 Mineralization Processes

Carbon mineralization is one of the carbon capture utilization, and storage (CCUS) technologies that can be used to capture large quantities of CO2 and convert it into stable carbonate products that can be stored easily. Several CO2 mineralization processes have been proposed; however, there are no commercial-scale projects because there are still significant issues that need to be improved before commercialization can take place. In this work, we evaluate the CO2 and energy penalties related to the most well-known types of mineralization processes developed to date, in which the mineralization reaction takes place directly under aqueous conditions, high pressures and temperatures, and compared these with newer T-P swing processes and ball-mill reactor processes, which are under development. The data used in the evaluation are taken from published literature. By comparing the three processes, we identify important variables that contribute to high CO2 and energy penalties so that future research can focus on optimization of these variables. It is observed that slurry concentration (heating) and particle size (grinding) are critical factors, with mineral calcination and operating pressure constituting other important factors.