Advances in optimal design and retrofit of chemical processes with uncertain parameters - Applications in design of heat exchanger networks
Doctoral thesis, 2023
There is widespread consensus that the omnipresent climate crisis demands humanity to rapidly reduce global greenhouse gas (GHG) emissions.
To allow for such a rapid reduction, the industrial sector as a main contributor to GHG emissions needs to take immediate actions.
To mitigate GHG emissions from the industrial sector, increasing energy efficiency as well as fuel and feedstock switching, such as increased use of biomass and (green) electricity, are the options which can have most impact in the short- and medium-term.
Such mitigation options usually create a need for design of new or redesign of existing processes such as the plant energy systems.
The design and operation of industrial plants and processes are usually subject to uncertainty, especially in the process industry.
This uncertainty can have different origins, e.g., process parameters such as flow rates or transfer coefficients may vary (uncontrolled) or may not be known exactly.
This thesis proposes theoretical and methodological developments for designing and/or redesigning chemical processes which are subject to uncertain operating conditions, with a special focus on heat recovery systems such as heat exchanger networks.
In this context, this thesis contributes with theoretical development in the field of deterministic flexibility analysis.
More specifically, new approaches are presented to enhance the modelling of the expected uncertainty space, i.e., the space in which the uncertain parameters are expected to vary.
Additionally, an approach is presented to perform (deterministic) flexibility analysis in situations when uncertain long-term development such as a switch in feedstocks interferes with operational short-term disturbances.
In this context, the thesis presents an industrial case study to i) show the need for such a theoretical development, and ii) illustrate the applicability.
Aside of advances in deterministic flexibility analysis, this thesis also explores the possibility to combine valuable designer input (e.g. non-quantifiable knowledge) with the efficiency of mathematical programming when addressing a design under uncertainty problem.
More specifically, this thesis proposes to divide the design under uncertainty problem into a design synthesis step which allows direct input from the designer, and several subsequent steps which are summarized in a framework presented in this thesis.
The proposed framework combines different approaches from the literature with the theoretical development presented in this thesis, and aims to identify the optimal design specifications which also guarantee that the the final design can operate at all expected operating conditions.
The design synthesis step and the framework are decoupled from each other which allows the approach to be applied to large and complex industrial case studies with acceptable computational effort.
Usage of the proposed framework is illustrated by means of an industrial case study which presents a design under uncertainty problem.
To allow for such a rapid reduction, the industrial sector as a main contributor to GHG emissions needs to take immediate actions.
To mitigate GHG emissions from the industrial sector, increasing energy efficiency as well as fuel and feedstock switching, such as increased use of biomass and (green) electricity, are the options which can have most impact in the short- and medium-term.
Such mitigation options usually create a need for design of new or redesign of existing processes such as the plant energy systems.
The design and operation of industrial plants and processes are usually subject to uncertainty, especially in the process industry.
This uncertainty can have different origins, e.g., process parameters such as flow rates or transfer coefficients may vary (uncontrolled) or may not be known exactly.
This thesis proposes theoretical and methodological developments for designing and/or redesigning chemical processes which are subject to uncertain operating conditions, with a special focus on heat recovery systems such as heat exchanger networks.
In this context, this thesis contributes with theoretical development in the field of deterministic flexibility analysis.
More specifically, new approaches are presented to enhance the modelling of the expected uncertainty space, i.e., the space in which the uncertain parameters are expected to vary.
Additionally, an approach is presented to perform (deterministic) flexibility analysis in situations when uncertain long-term development such as a switch in feedstocks interferes with operational short-term disturbances.
In this context, the thesis presents an industrial case study to i) show the need for such a theoretical development, and ii) illustrate the applicability.
Aside of advances in deterministic flexibility analysis, this thesis also explores the possibility to combine valuable designer input (e.g. non-quantifiable knowledge) with the efficiency of mathematical programming when addressing a design under uncertainty problem.
More specifically, this thesis proposes to divide the design under uncertainty problem into a design synthesis step which allows direct input from the designer, and several subsequent steps which are summarized in a framework presented in this thesis.
The proposed framework combines different approaches from the literature with the theoretical development presented in this thesis, and aims to identify the optimal design specifications which also guarantee that the the final design can operate at all expected operating conditions.
The design synthesis step and the framework are decoupled from each other which allows the approach to be applied to large and complex industrial case studies with acceptable computational effort.
Usage of the proposed framework is illustrated by means of an industrial case study which presents a design under uncertainty problem.
Design under Uncertainty
Uncertainty Space
Process Integration
Managing Variations
Process Design
Flexibility Analysis
Adress on campus: Hörsal HC3, Hörsalsvägen 16; Online: Button below, password: HXNOpt Technical support: hodel@chalmers.se
Opponent: Univ.Prof. Dipl.-Ing. Dr.techn. René Hofmann, TU Wien, Austria
Author
Christian Langner
Chalmers, Space, Earth and Environment, Energy Technology
A computational tool for guiding retrofit projects of industrial heat recovery systems subject to variation in operating conditions
Applied Thermal Engineering,;Vol. 182(2021)
Journal article
A framework for flexible and cost-efficient retrofit measures of heat exchanger networks
Energies,;Vol. 13(2020)
Journal article
Costs vs. Flexibility of Process Heat Recovery Solutions Considering Short-Term Process Variability and Uncertain Long-Term Development
Frontiers in Chemcial Engineering,;Vol. 3(2021)
Journal article
Flexibility analysis of chemical processes considering overlaying uncertainty sources
Computer Aided Chemical Engineering,;Vol. 49(2022)p. 769-774
Paper in proceeding
Flexibility analysis using boundary functions for considering dependencies in uncertain parameters
Computers and Chemical Engineering,;Vol. 174(2023)
Journal article
Langner, C., Svensson, E., Papadokonstantakis, S., & Harvey, S. Novel reformulations for modelling uncertainty and variations in a framework for chemical process design
The future of industry is uncertain – but we can fix it!
Human activities have led to a dramatic increase in greenhouse gas (GHG) emissions, which trap heat in the atmosphere and cause the global average temperature to rise. This rise in the average temperature is putting our planet's ecosystems in grave danger. To prevent the most severe consequences of climate change, countries signed the Paris Agreement in 2015, committing themselves to limit the increase in global temperature to well below 2 °C and ideally to 1.5 °C. However, this requires rapid and sustained reductions in GHG emissions, including non-carbon dioxide emissions.
The industrial sector is responsible for the majority of GHG emissions, with fuel combustion, process emissions, and indirect emissions related to heat and electricity use being major contributors. Improving energy efficiency has proven to be an important way to mitigate GHG emissions from the industrial sector, and other options include using cleaner sources of energy, such as biomass and green electricity, as well as carbon capture and storage.
Implementing such measures to mitigate GHG emissions in industry often requires designing new processes as well as redesigning existing ones, which can be challenging due to uncertainties such as unknown or varying process parameters. Additionally, product quality and quantity constraints can make it difficult to pursue GHG mitigation potentials. Despite these challenges, taking action to reduce GHG emissions is crucial for preventing the worst consequences of climate change.
This thesis proposes new theoretical and methodological developments aimed at aiding the design of chemical processes that are capable of handling variations in operating conditions, with a specific focus on heat recovery systems. More specifically, this thesis proposes a more accurate approach to account for parameter uncertainty in chemical processes compared to traditional approaches. Having an accurate representation of the expected uncertainties is vital for assessing whether a process design can accommodate all anticipated operating conditions and for avoiding unnecessary overdesign of process equipment. The proposed methods allow for considering short-term variations, such as disturbances expected during normal operation, as well as uncertainties introduced through planned long-term development, such as an increase in production capacity.
Additionally, this thesis puts forth a new approach to design or redesign chemical processes commonly found in industrial plants. Such processes can be very complex due to many interconnections with other processes which often requires highly individualized solutions. Consequently, the proposed approach enables the incorporation of non-quantifiable knowledge, such as input from the operating engineers while ensuring that the final process design can accommodate all expected variations in operating conditions.
Human activities have led to a dramatic increase in greenhouse gas (GHG) emissions, which trap heat in the atmosphere and cause the global average temperature to rise. This rise in the average temperature is putting our planet's ecosystems in grave danger. To prevent the most severe consequences of climate change, countries signed the Paris Agreement in 2015, committing themselves to limit the increase in global temperature to well below 2 °C and ideally to 1.5 °C. However, this requires rapid and sustained reductions in GHG emissions, including non-carbon dioxide emissions.
The industrial sector is responsible for the majority of GHG emissions, with fuel combustion, process emissions, and indirect emissions related to heat and electricity use being major contributors. Improving energy efficiency has proven to be an important way to mitigate GHG emissions from the industrial sector, and other options include using cleaner sources of energy, such as biomass and green electricity, as well as carbon capture and storage.
Implementing such measures to mitigate GHG emissions in industry often requires designing new processes as well as redesigning existing ones, which can be challenging due to uncertainties such as unknown or varying process parameters. Additionally, product quality and quantity constraints can make it difficult to pursue GHG mitigation potentials. Despite these challenges, taking action to reduce GHG emissions is crucial for preventing the worst consequences of climate change.
This thesis proposes new theoretical and methodological developments aimed at aiding the design of chemical processes that are capable of handling variations in operating conditions, with a specific focus on heat recovery systems. More specifically, this thesis proposes a more accurate approach to account for parameter uncertainty in chemical processes compared to traditional approaches. Having an accurate representation of the expected uncertainties is vital for assessing whether a process design can accommodate all anticipated operating conditions and for avoiding unnecessary overdesign of process equipment. The proposed methods allow for considering short-term variations, such as disturbances expected during normal operation, as well as uncertainties introduced through planned long-term development, such as an increase in production capacity.
Additionally, this thesis puts forth a new approach to design or redesign chemical processes commonly found in industrial plants. Such processes can be very complex due to many interconnections with other processes which often requires highly individualized solutions. Consequently, the proposed approach enables the incorporation of non-quantifiable knowledge, such as input from the operating engineers while ensuring that the final process design can accommodate all expected variations in operating conditions.
Framtiden för svenska kraftvärmeverk - en utvärdering av möjliga energi- och klimattjänster samproducerade med det svenska fjärrvärmebehovet
ÅForsk (Ref.nr20-304), 2020-06-01 -- 2024-09-30.
Flexible process integration solutions for the pulp and paper industry
Södra, 2017-01-01 -- 2019-12-31.
Swedish Energy Agency (42326-1), 2017-01-01 -- 2019-12-31.
Subject Categories
Energy Engineering
Chemical Engineering
Areas of Advance
Energy
ISBN
978-91-7905-864-7
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5330
Publisher
Chalmers
Adress on campus: Hörsal HC3, Hörsalsvägen 16; Online: Button below, password: HXNOpt Technical support: hodel@chalmers.se
Opponent: Univ.Prof. Dipl.-Ing. Dr.techn. René Hofmann, TU Wien, Austria