[Purdue logo]<https://maillinks.purdue.edu/t/48602565/1705063408/63490351/0/1006672/?x=b92ea84d> Davidson School of Chemical Engineering<https://maillinks.purdue.edu/t/48602565/1705063408/72334380/0/1006672/?x=18562fd0> Graduate Faculty Lectureship Seminar<https://maillinks.purdue.edu/t/48602565/1705063408/99262836/0/1006672/?x=4cda0a7b> Dr. Akash Nogaja Dear All, On behalf of Purdue University's Davidson School of Chemical Engineering, we are glad to announce our upcoming seminar. [cid:02ae7244-64d2-49b9-9803-6a518c1767e9] Dr. Akash Nogaja Senior Research Specialist Dow Chemical Bio: Akash Nogaja received his bachelor’s degree in Chemical Engineering from the Institute of Chemical Technology (ICT), Mumbai, India, in June 2020. In Fall 2020, he joined the PhD program in the Davidson School of Chemical Engineering at Purdue University, West Lafayette, Indiana. Under the guidance of Dr. Rakesh Agrawal and Dr. Mohit Tawarmalani, his research focused on the design and optimization of energy-efficient distillation systems, with particular emphasis on heat pump integration and thermodynamic analysis. During his time at Purdue, Akash received multiple departmental and conference awards, including the Centennial Fellowship and the Faculty Lectureship Award from the Davidson School of Chemical Engineering, as well as the AIChE Separations Division Graduate Student Research Award. Akash is currently working as a Senior Research Specialist in the Machine Learning, Optimization, and Statistics (MiLOS) group at Dow Chemical. His work focuses on supply chain optimization, scheduling and logistics planning, and process design.  "Advances in the design of Heat Integrated and Heat Pump Assisted Distillation Systems " Thursday, February 12, 2026 3:00 p.m. - 4:15 p.m. ET FRNY G140 – Reception at 2:30 p.m. in Henson Atrium – Abstract: The push for lower greenhouse gas emissions, combined with the increasing availability of carbon-free electricity, has motivated the electrification of energy-intensive chemical processes, particularly distillation systems, which account for approximately 6-10% of industrial energy consumption in the US. Direct electrification via resistive heating is impractical, making heat pump–assisted distillation a more energy-efficient and viable pathway for decarbonization. Despite their advantages, heat pumps have seen limited adoption in above-ambient distillation systems due to historically low fossil fuel prices, high compressor capital costs, and the inherent complexity of multicomponent separations. In such systems, the number of feasible distillation configurations grows combinatorially with the number of components, and these alternatives can differ significantly in energy demand. Exhaustive evaluation using commercial simulators is computationally prohibitive, while heuristic or sequential design approaches often lead to suboptimal, energy-intensive solutions. This research addresses these challenges through three key contributions: (i) enabling heat integration between condensers and reboilers in multicomponent distillation (ii) developing accurate and computationally tractable surrogate models for temperature prediction and heat pump work estimation suitable for global optimization, and (iii) formulating optimization frameworks to identify globally optimal configurations under practical constraints. The temperature surrogate model, derived from the Antoine equation under the assumption of constant relative volatility, captures nonlinear phase-change behavior with an accuracy of R2 = 0.99. Additionally, this work delineates the conditions under which exergy loss serves as a valid proxy for heat pump work and introduces the concept of Minimum Heat Pump Work, providing a tighter theoretical lower bound on compressor energy requirements. Case studies demonstrate that the proposed framework identifies non-intuitive, economically viable configurations capable of substantially reducing energy consumption and carbon emissions. 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