Graduate Student Seminar | A unified strategy for chemical and biological process scale-up by Syed Soheil

Biography:

Dr. Seyed Soheil Mansouri is an Associate Professor in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark

(DTU) and affiliate faculty at Sino-Danish Center for Education and Research in Beijing, China. He received his PhD (2016) and MSc (2013) in chemical and biochemical engineering both from DTU. His current research is focused on Process Systems Engineering, System Dynamics, Computational Agility (AI and Quantum Computing), Socio-Economic-Technological Analysis of Complex Dynamic Systems, Process Synthesis, Design, Control and Intensification with focus on Chemical, Pharmaceutical and Bio-manufacturing sectors. He is a senior member of American Institute of Chemical Engineers (AIChE) and Danish representative to Computer Aided Process Engineering (CAPE) Working Party of European Federation of Chemical Engineers (EFCE). He serves as treasurer and board member of European Committee for the Use of Computers in Chemical Engineering Education (EURECHA). His is also international exchange coordinator for AIM-Bio project, 27 million USD project between DTU and NC State University funded by Novo Nordisk Foundation. Furthermore, he is involved in Biosolutions Zealand as a major partner to develop a biomanufacturing powerhouse in Zealand Region of Denmark with a total funding of close to 12 million USD funded by European Regional Development Fund. Dr Mansouri develops methods and tools within the domain of Process Systems Engineering (PSE). This is an interdisciplinary field within Chemical and Biochemical Engineering that its main objective is development of systematic procedures based on mathematical models and computational techniques for the analysis, design, operation, control and optimization of process systems. Recently, he has engaged in the Biosolutions Zealand project to design, develop and tackle the economics of scale associated with production systems for biomolecule scale-up combining PSE and pilot-scale validation. He is also engaged in range of strategic partnerships with international companies such as 21st.BIO (Denmark), Henkel AG (Germany), and Zapata Computing (USA).

Process engineering requires information at various levels of granularity from the microscale to the macroscale including reaction mechanism

(catalytic) and/or cell metabolic pathways (biocatalytic), reaction kinetics, reactor design specifications, transport phenomena, and interaction of the microscale to the macroscale and vice-versa. While many of these phenomena can be modeled mechanistically, engineering towards a goal of maximizing final process throughput, and other constraint satisfaction, requires tools from artificial intelligence such as search, optimization and sampling, and machine learning to model process correlations. The complexity is further enhanced due to the unavailability of data from many critical microscale variables such as metabolic fluxes, cell morphology, cell viability, etc.

This is not disregarding the stochasticity in the dynamics of the individual cells and observability limitations of the process.

More specifically, industries involved in the production of very large quantities (commodities, foods, etc.), suffer from low profit margin thereby making the economic /environmental feasibility very sensitive to for example

(i) scale of production and thus the scale-up process, (ii) raw material prices, and (iii) energy prices. Biosolutions, production of bio-based commodity and specialty products, often lean towards large scales of production and low value raw materials (e.g., valorization of “waste”

streams such as CO2 and process water). The stochasticity in the dynamics lends itself to the use systematic strategies to decompose the complexities for process characterization. The field of bioprocess engineering and an ever increasing number of new biomolecules discovered in the lab, entails to a necessity to develop strategies for a faster time-to-market.

Here, a strategy to bridge across scales (from micro- to macro) for bringing a product from lab to pilot-scale and eventually full implementation will be presented. More specifically, first an overview of the status within process systems engineering will be given. Next, an overview of a systematic methodology for bioprocess scale-up taking advantage of a combination of mechanistic-data-driven modeling strategies and validation and qualification with non-invasive measurement technologies such as capacitance, NIR and image processing will be highlighted.