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Internal pricing in integrated energy system design

Frontiers in Energy Research 2025年 13卷

作者： Granacher, Julia Schnidrig, Jonas Chuat, Arthur Lepour, Dorsan Terrier, Cédric Castro-Amoedo, Rafael Marechal, Francois M. A. Industrial Process and Energy Systems Engineering group École Polytechnique Fédérale de Lausanne Sion Switzerland CIRAIG Institue of Energy and Environment University of Applied Sciences Western Switzerland Sion Switzerland

Environmentally sustainable and economically viable process and energy systems are imperative to a successful energy transition. Often, design configurations are derived from a global perspective, in which the individual needs and interests of actors within the system are overlooked. This work proposes an approach for designing a system considering its entire scope and acknowledging the individual actors within the system. System solutions are generated from the perspective of a universal decision-maker who is aware of the whole system, and the obtained solution space is analyzed regarding implications for the individual actors. Thereby, prices of internal exchanges between actors that would allow for the realization of the optimal integrated system solution while granting each actor their economic objectives are derived. The approach is demonstrated in three distinct case studies varying in size: a bio-based industrial site, a renewable energy community, and a national energy system. All case studies yield system configurations allowing the actors to profit from economic benefits emerging from synergies from internal cooperation. Further research must delve into diverse system settings and actor paradigms to enhance the robustness and applicability of the derived insights. Copyright © 2025 Granacher, Schnidrig, Chuat, Lepour, Terrier, Castro-Amoedo and Maréchal.

关键词： Integrated circuit design

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Game-theoretical explorations of the mesoscale flow structure and regime transitions in bubble columns

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Particuology 2020年第1期18卷 100-108页

作者： Shuo Yuan Yifen Mu Xiaoping Guan Zhixin Liu Jianhua Chen Ning Yang Lin Zhang Key Lab of Systems and Control Institute of Systems SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Understanding the mesoscale structure and regime transition in bubble columns is of great significance for reactor design and *** on the energy-minimization multiscale(EMMS)model,a noncooperative game model with constraints is proposed to investigate the structural properties of gas-liquid systems in which small and large bubbles are chosen as players and the energy consumption form the objective *** conservation equations of the system can be regarded as the constraints of the *** the formulated noncooperative game model,the concept of the generalized Nash equilibrium(GNE)is used to characterize the *** algorithm is developed to numerically compute the GNE and some important structural parameters in the *** numerical results show the existence of the GNE for all values of the superficial gas velocity *** Ug varies,the trends in the state variables can be observed and the critical point of Ug *** overall trend of the flow regime transition agrees with the original EMMS model and experimental results,although the GNE calculation also reveals different single-bubble dominant mechanisms with increasing Ug.

关键词： Bubble column Mesoscale Regime transition Game theory Generalized Nash equilibrium EMMS model

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First generation of translucent monoliths for photochemical applications

Journal of Advanced Manufacturing and Processing

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Journal of Advanced Manufacturing and processing 2020年第2期2卷 1-11页

作者： Jacobs, Mathias Kayahan, Emine Thomassen, Leen C.J. Kuhn, Simon Leblebici, M. Enis Center for industrial process technology Department of Chemical Engineering KU Leuven Diepenbeek Belgium Process Engineering for Sustainable Systems Department of Chemical Engineering KU Leuven Leuven Belgium

Monolith structures are a common method to scale flow chemistry tools up to industrial throughput. Internally illuminated monolith reactors (IIMRs) were their versions for photochemistry. The channels inside the monolith were illuminated with optical fibers coated with a photocatalyst such as TiO2. The majority of the light in an IIMR was however not utilized to illuminate the reaction medium. Furthermore, the monolith was always made of opaque materials, making it impossible for unabsorbed light to illuminate other channels, lowering the photochemical space-time yield (PSTY), a benchmark for the overall energy efficiency of a photoreactor. In this paper, the potential of a translucent monolith structure for scalable photochemical process design was tested. Due to the translucent nature of the monolith, there was no need for optical fibers or multiple light sources to provide the necessary photon flux and distribution. There has never been a study of translucent monoliths in the open literature and no design parameters have been investigated. This research studies the first translucent monoliths and their design principles by varying the channel size, channel distance as well as the amount of unit cells with a ray tracing algorithm in COMSOL Multiphysics. A model was developed, validated and used to design a monolith for the photodegradation of micropollutants in a case study. A theoretical improvement of seven orders of magnitude in PSTY was obtained compared to IIMRs making this reactor design a promising candidate for more efficient photochemical processes. © 2020 American Institute of Chemical Engineers

关键词： Multiphysics

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Driving Towards Net-Zero: Leveraging Machine Intelligence for Robust Optimization of Coal and Combined Cycle Gas Power Stations

SSRN

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SSRN 2024年

作者： Ashraf, Waqar Muhammad Dua, Vivek The Sargent Centre for Process Systems Engineering Department of Chemical Engineering University College London Torrington Place LondonWC1E 7JE United Kingdom

The fossil-based power stations using coal and natural gas are envisioned to support the peak energy demand for the planning of net-zero energy-mix in different economies around the globe. However, the operation optimization of the fossil-based power systems, which is quite challenging, can significantly reduce the emissions discharge to further support the net-zero goal. Herein, we present the data-driven robust optimization framework built on machine learning and two-step robust optimization approach for the operation optimization of the coal and gas power stations. The Data Information integrated Neural Network (DINN) models are trained using the operation data of the coal and gas power stations with modelling accuracy more than 0.85 to predict thermal efficiency, power and heat rate of the power stations, and are also validated on the plants’ operation with good accuracy. The model-driven variables significance analysis reveals that steam cycle-based variables have the percentage significance of 79%, 74% and 66%, while gas turbines have the percentage significance of 85%, 93% and 78% towards the thermal efficiency, power and heat rate of coal and gas plants respectively. The two-step robust optimization analysis is carried out to estimate the robust optimal solution at different generation capacities of the two power stations. The optimization driven results are verified on the plants’ operation and improved power stations’ performance is achieved that leads to the highest annual reduction in CO2 measuring 200 ± 10 kt and 62 ± 20 kt respectively corresponding to mid-load capacity generation operation of the coal and combined cycle gas power station. The machine intelligence and optimization approach synergized by domain knowledge of the plants operation can significantly contribute to enhance the performance of the power complexes that supports the net-zero objective from the energy sector. © 2024, The Authors. All rights reserved.

关键词： Coal

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A hybrid strategy for solving cubic equations of state based on a novel root discriminant method

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Fluid Phase Equilibria 2025年 597卷

作者： Zhao, Jun Zhang, Luoyu Wang, Lili Chen, Yushi Zhao, Wenying Tao, Shaohui Xiang, Shuguang Institute of Process Systems Engineering Qingdao University of Science and Technology ShanDong266042 China Petro-Cyber Works Information Technology Co. Ltd Shanghai200050 China

The cubic equations of state (CEoS) is one of the most fundamental and popular models in chemical process simulation. This study aims to explore the impact of CEoS solution methods on the speed of process simulation and propose a simple and fast method to determine the single real root of CEoS at a single-phase state. Utilizing the advantages of this method, a hybrid CEoS solution strategy based on Cardano-Tartáglia's formula and Newton-Raphson method (HCTNR) was developed. The average computation time is approximately 37 % of that of the Cardano-Tartáglia's formula. The calculation speed of the new method is faster than that of the well-known Halley's method. Extensive examples of chemical process simulation were used to verify the speed and robustness of this method, and the results indicate that HCTNR can effectively improve the speed of process simulation while ensuring the accuracy of the calculation results. © 2025

关键词： Equations of state of solids

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Investigating the Hydrogen Demand Curve in Road Transport

Investigating the Hydrogen Demand Curve in Road Transport

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International Conference on European Electricity Market, EEM

作者： Lin Zheng Johannes Eckstein Bastian Weißenburger Copernicus Institute of Sustainable Development Utrecht University Utrecht The Netherlands Fraunhofer Institute for Systems and Innovation Research ISI Karlsruhe Germany Chair of Energy Systems Analysis Institute of Energy and Process Engineering ETH Zurich Zurich Switzerland

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

Applying hydrogen can potentially decarbonize road transport. This paper introduces a novel approach for deriving hydrogen demand curves in road transport. By analyzing acceptable prices for new vehicle buyers opting for hydrogen and the possible hydrogen demand from these new vehicles, our model provides insights into potential market dynamics. The model is applied to Germany and simulates the total hydrogen demand across 15 price scenarios. The results reveal that hydrogen demand can be expected from passenger transport in 2030 but not in 2045, attributed to the economic viability and increasing availability of electric alternatives. Within freight transport, heavy-duty freight emerges as a key segment with significant potential demand at relatively high acceptable prices. Comparison with a prior study underscores the model's contribution, offering insights into the upper bounds of acceptable hydrogen price.

关键词： Economics Electric potential Analytical models Upper bound Roads Hydrogen Europe

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Predicting Partial Discharges of Transformers: Decision Support System for Factory Acceptance Test

Predicting Partial Discharges of Transformers: Decision Supp...

引用

2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Gigerl, B. Zhao, Y. Raminger, J. Bakakeu, J. Kern, R. Thalmann, S. Siemens Energy Department of Process Digitalization Weiz Austria Siemens Energy Department of Manufacturing Technology Nürnberg Germany Siemens Energy Department of Test Field Weiz Austria Alteryx Department of Sales Engineering München Germany Institute of Interactive Systems and Data Science Technical University of Graz Graz Austria Institute of Operations and Information Systems University of Graz Graz Austria

ISBN: (纸本)9798350323153

Partial discharges, mainly caused by an insufficient drying processes or different types of contamination, reduce the lifetime of a transformer, and thus lead to expensive rework costs. Herein, a decision support system for partial discharges of transformers occurring in oven and filling processes is introduced. Based on machine learning (ML), partial discharge results are evaluated in dependency of various manufacturing parameters and an automated prediction tool to guide the production with preventive actions is developed. The required data, obtained from sensors and manufacturing sources, is used to train supervised learning algorithms that aim to predict and classify partial discharges. To achieve adequate accuracy and reliability, multiple ML and data mining techniques are applied, including feature engineering, clustering, and final evaluation of performance by cost factors. The evaluation results show that the introduced ML models effectively detect and classify early test failures in oven and filling processes, resulting in a successful identification of key factors and consequently to more efficient action derivation. Overall, the potential of decision support systems as a valuable tool in the field of transformers is emphasized. © 2023 IEEE.

关键词： Decision Support System Machine Learning Partial Discharges Predictive Quality control

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Stimuli-Responsive lonic Liquids and the Regulation of Aggrega-tion Structure and Phase Behavior

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Chinese Journal of Chemistry 2021年第3期39卷 729-744页

作者： Xiao-Qing Yuan Ya-Qin Zhang Zhi-Yong Li Feng Huo Yi-Hui Dong Hong-Yan He Beijing Key Laboratory of lonic Liquids Clean Process CAS Key Laboratory of Green Process and EngineeringState Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBejling 100190China Dalian National Laboratory for Clean Energy DalianLiaoning 116023China Collaborative Innovation Center of Henan Province for Green Manufacturing of Fine Chemicals Key Laboratory of Green Chermical Media and ReactionsMinistry of EducationSchool of Chemistry and Chemical EngineeringHenan Normal UniversityXinxiangHenan 453007China

lonic liquids with stimuli-responsive characteristics are an essential branch in the comprehensive ionic liquids'family,which can meet the controllable and reversible needs in practical applications with different stimulation *** this review,we summarize the recent research progress of these stimuli-responsive ionic liquids,mainly focusing on their chemical structures,properties,responsive mechanism,aggregation structures,and phase behaviors,which were driven by external stimuli,such as CO2,magnetism,light,temperature,redox,and ***,the particular or potential applications of stimuli-responsive ionic liquids are expounded in various fields of science,including catalytic reaction,extraction,separation,nano-material preparation,and gas *** the end,the challenges and strategies of the subject are briefly *** is expected that this review will contribute to the rational design and applications of stimu-liresponsive ionic liquids in the future.

关键词： lonic liquids Stimuli Responsive Aggregation Phase behavior

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A methodological procedure for the energy transition in the land transport sector by quantifying renewable energy needs

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Energy Reports 2025年 13卷 6042-6058页

作者： Yánez-Rosales, Pablo Schallenberg-RodrÍguez, Julieta del Río-Gamero, B. Process Engineering Department Industrial and Civil Engineering School Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain Group for the Research on Renewable Energy Systems University of Las Palmas de Gran Canaria Spain

A significant increase in the electricity demand from the transport sector is expected in the coming years given the decarbonisation targets set by most countries. With a view to guaranteeing a sustainable transition, in this study a novel methodology with a cross-sectoral (transport and renewable energies) coordination is proposed to determine the renewable power required to meet the needs of the transport sector. Two scenarios are defined: a Trend Scenario and an Implemented Policy Scenario in which the regional targets in these areas must be achieved. The Implemented Policy Scenario entails a modal shift from private vehicle to other transport modes, such as collective or active. The methodology is applied to Tenerife Island (Canary Archipelago, Spain) where the Spanish government target of 28 % electrification of land transport is assumed. On the basis of a 2030 time horizon, the variation in the total number of vehicles ranges from a 19 % increase (Trend Scenario) to a 0.5 % reduction (Implemented Policy Scenario). The Trend Scenario results in a 2.5 % increase in the electricity demand compared to 2020, while the Implemented Policy Scenario would require a 19 % increase. This would mean an additional installed renewable capacity in Tenerife of 41 MW and 322 MW, respectively. Reaching the goals in this region would entail a reduction of around 430 kton CO2-eq. The proposed methodology enables identification of key socioeconomic parameters, including vehicle purchase acquisition, vehicle annual operational costs, gross domestic product and population, as well as their impact on the land transport transition. © 2025 The Authors

关键词： Vehicles

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Streamlining Energy Transition Scenarios to Key Policy Decisions

arXiv

引用

arXiv 2023年

作者： Baader, F.J. Moret, S. Wiesemann, W. Staffell, I. Bardow, A. Energy & Process Systems Engineering Department of Mechanical and Process Engineering ETH Zürich Switzerland Centre for Environmental Policy Imperial College London United Kingdom Imperial College Business School Imperial College London United Kingdom

Uncertainties surrounding the energy transition often lead modelers to present large sets of scenarios that are challenging for policymakers to interpret and act upon. An alternative approach is to define a few qualitative storylines from stakeholder discussions, which can be affected by biases and infeasibilities. Leveraging decision trees, a popular machine-learning technique, we derive interpretable storylines from many quantitative scenarios and show how the key decisions in the energy transition are interlinked. Specifically, our results demonstrate that choosing a high deployment of renewables and sector coupling makes global decarbonization scenarios robust against uncertainties in climate sensitivity and demand. Also, the energy transition to a fossil-free Europe is primarily determined by choices on the roles of bioenergy, storage, and heat electrification. Our transferrable approach translates vast energy model results into a small set of critical decisions, guiding decision-makers in prioritizing the key factors that will shape the energy transition. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision trees

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