7/23/2019 Flyer for RUppalari

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Oklahoma State UniversitySchool of Chemical Engineering

Tuesday, September 15, 20152:00 P.M.

ATRC 102

Ramagopal Uppaluri, PhDDepartment of Chemical EngineeringIndian Institute of Technology (IIT) GuwahatiGuwahati, Assam, India

Dr. Uppaluri received his Ph.D. in Process Optimization from the University of Manchester, U. K. (2002), and an

M.Tech. (Chemical Engineering) from the Indian Institute of Technology, Kanpur, India (1999). After a brief

post-doctoral research position at Robert Gordon University at Aberdeen, Scotland (2002 2004), Dr. Uppaluri joined

the Indian Institute of Technology (IIT), Guwahati in August 2004. Dr. Uppaluri has diverse interests in several areas

of chemical engineering science and materials research. These include low cost ceramic membranes, dense Pd metalcomposite membrane fabrication, microfiltration and ultrafiltration of fruit juices, process optimization, novel

optimization techniques and their engineering applications, petroleum refinery engineering, polymer-natural fiber

Composites, and surfactant enhanced oil recovery. His commitment and professionalism in Chemical Engineering

teaching and research enabled him to become one of the youngest Associate Professors and Professors of IIT Gu-

wahati.

Optimization of Hybrid Multi-stage Flash (MSF) and Reverse Osmosis (RO) Processes UsingDifferential Evolution Algorithm

The optimization of hybrid MSF-RO processes involves non-linear programming deterministic approaches.

Deterministic non-

linear programming formulations often suffer with the assurance of solution quality with respect tothe global optimal domain. Thereby, non-deterministic methods such as genetic algorithm (GA) and simulated

annealing (SA) have gained popularity in recent times. Given the limited investigations carried out to date in the field

of NLP optimization of hybrid MSF-RO processes, the presentation summarizes modeling approaches for the optimal

design of hybrid MSF-RO processes. Twenty alternate MSF-RO processes have been conceptualized for the evaluation

of their global optimality and subsequent ranking to achieve lowest freshwater production cost. Literature process

models have been incorporated to represent MSF and RO performance models and cost functions. Total

annualized freshwater production cost has been considered as the objective function during optimization.

Differential evolution algorithm has been applied in the MATLAB programming environment. Inequality constraints

have been effectively handled using a penalty function approach. For comparative purposes, the results obtained from

the DE based approach have been compared with the MATLAB built-in non-deterministic programming approaches

(Genetic algorithms and Simulated Annealing). Analyzation has shown that novel hybrid MSF-RO process

configurations exist whose optimality is better than those reported in the literature. Further, the MATLAB based GA

and SA optimizers were not effective to achieve feasible solutions (solutions without penalty). Thereby, the efficacy of

DE to effectively tackle relevant higher number of inequality constraints has been proven from the investigations and

case studies which have been carried out. Another important inference of the research is that hybrid MSF-RO

processes do not offer economic competence with respect to the RO processes. These inferences confirm the relevance

of global optimization approaches for desalination process network optimization.

Lecture begins at 2:00 P.M., 102 ATRCReception to follow