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Public thesis defense: Maziar Kermani

© 2018 EPFL

© 2018 EPFL

On Friday 12. October 2018, Maziar Kermani successfully defended his PhD thesis in front of a public audience. The thesis was nominated for three prizes including best EPFL thesis, best thesis of the EDEY doctoral school, and the award of the European Federation of Chemical Engineering (EFCE).


Efficient consumption of energy and material resources, including water, is the primary focus for process industries to reduce their environmental impact. The Conference of Parties in Paris (COP21) highlighted the prominent role of industrial energy efficiency in combatting climate change by reducing greenhouse gas (GHG) emissions. Consumption of energy and material resources, especially water, are strongly interconnected; and therefore, must be treated simultaneously using a holistic approach to identify optimal solutions for efficient processing. Such approaches must consider energy and water recovery within a comprehensive process integration framework which includes options such as organic Rankine cycles for electricity generation from low to medium temperature heat. This thesis addresses the issue of how to efficiently manage energy and water in industrial processes by presenting two systematic methodologies for the simultaneous optimization of heat and mass and combined heat and power production. A novel iterative sequential solution strategy is proposed for optimizing heat-integrated water allocation networks through decomposing the overall problem into three sub-problems using mathematical programming techniques. The approach is capable of proposing a set of potential energy and water reduction opportunities that should be further evaluated for technical, economical, physical, and environmental feasibilities. A novel and comprehensive superstructure optimization methodology is proposed for organic Rankine cycle (ORC) integration in industrial processes including architectural features, such as turbine-bleeding, reheating, and transcritical cycles. Meta-heuristic optimization (via a genetic algorithm) is combined with deterministic techniques to solve the problem: by addressing fluid selection, operating condition determination, and equipment sizing. This thesis further addresses the importance of holistic approaches by applying the proposed methodologies on a kraft pulp mill. In doing so, freshwater consumption is reduced by more than 60%, while net power output is increased by a factor of six. The results exhibit that interactions among these elements are complex and therefore underline the necessity of such methods to explore their optimal integration with industrial processes. The potential implications of this work are broad, extending from total site integration to industrial symbiosis.