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Technical and digital twin concept of an industrial heat transfer station for low exergy waste heat
Abstract
Within the German industrial sector, more than 70 % of final energy is used to supply processes with thermal energy. Nonetheless within manufacturing systems substantial quantities of heat are released as low exergy waste heat due to technical and organizational barriers. This paper presents a concept and digital twin model for an industrial heat transfer station connecting industrial thermal networks with district heating systems to integrate low exergy waste heat from production processes efficiently. A case study of an industrial site shows potential waste heat utilization of up to 70 % while reducing operating expenses by up to 6 %.
... Based on [Kohn21], this work calls a linkage a digital service, which is consistent with the terms digital master and digital shadow. ...
... The cyber-physical production system focuses on the design and operational life cycle phases and uses the CIRP definitions for cyber-physical production system [Mono18] and digital twin [Star18]. It extends the digital twin concept presented in [Kohn21]. For a detailed explanation of cyber-physical production systems see Section 2.3. ...
The share of fluctuating renewable energy in the electricity grid is increasing strongly, primarily in industrialised countries. The industrial sector accounts for a large share of the total electrical energy consumption and current research shows that it is possible to adapt this energy consumption to the fluctuating renewable energy generation using demand response (DR). Especially aqueous parts cleaning machines have a high DR potential. However, only a few approaches exist that show how DR measures can be implemented on real production machines.
This work develops a method that enables the execution of DR measures on aqueous parts cleaning machines. The so-called demand response automation architecture design (DRAAD) method consists of a DR potential analysis, a DR automation architecture including a DR automation program and a DR data model, as wall as a DR control algorithm.
The DR potential analysis analyses the technical DR potential of the machine components for the DR method store energy inherently and of the cleaning process for the DR method interrupt process. The DR potential analysis uses only the machine documentation and simple calculations, such that it can be carried out by employees of the machine manufacturer.
The framework for the DR automation architecture is a cyber-physical production system. This consists of the physical aqueous parts cleaning machine, its digital twin, external elements such as the energy market and a cyber-physical interface representing the communication in the cyber-physical production system. The digital twin includes the DR automation program, the DR data model and the DR process model, which is used by the DR control algorithm, in the digital master. The digital twin also includes a digital shadow and digital services.
The object-oriented DR automation program implements sensor, actuator, and system objects as well as functions that enable the execution of the DR methods store energy inherently and interrupt process. In addition to DR functions, functional safety functions are included. The communication between DR automation program and DR control algorithm is modelled in the DR data model. This includes all data points needed for the calculation (observing) and execution (controlling) of the two DR actions.
The DR control algorithm, a model predictive control algorithm, minimises the energy cost of the aqueous parts cleaning machine based on varying energy prices. Both DR measures are implemented and the approach is scalable and transferable to diff erent aqueous parts cleaning machines.
The DRAAD method is applied and validated on the aqueous parts cleaning machine MAFAC KEA in the ETA research factory. The DR potential analysis of the machine results in a DR power potential of 87 % of the machine’s rated power for store energy inherently and a DR energy power potential of 99 % of the energy consumption of the reference cleaning process for interrupt process. In the field test, a power change of 49 % and an energy shift of 82 % can be retrieved.
... For a resource efficiency of phosphorus production from apatitenepheline waste, Dli et al. (2020) offered a digital environment structure via digital twin technology. For an industrial heat transfer place, Kohne et al. (2021) presented a digital twin model. ...
... As mentioned above, we have extensively studied SWM4.0 issues and extracted the common innovative solutions based on I4.0 technologies from the SLR. These outcomes are: smart resource efficiency technology (Dli et al., 2021), smart monitoring system (Gopalakrishnan et al., 2020), smart bins (Bano et al., 2020), smart heating system (Kohne et al., 2021), smart waste collection (Fatimah et al., 2020b), food sharing (Mazzucchelli et al., 2021), smart waste to energy (Kabugo et al., 2020), smart waste transportation (Zeb et al., 2019), smart refrigerator (Wang et al., 2019), smart e-waste recycling (Wanderley & Bonacin, 2019), smart waste classification (Kazancoglu et al., 2020), automated detection garbage (Sunny et al., 2019), dynamic vehicle routing (Bebortta et al., 2020), smart detection hazardous waste (Yonghui Wu & Li, 2022), and smart landfill systems (Dey et al., 2022). Then, the role of four main pillars to successfully implement the solutions is examined by 12 waste management experts. ...
Smart Waste Management (SWM) discusses the waste management process for different types of waste while introducing an intelligent approach to controlling the amount of waste. This paper introduces SWM4.0, which applies Industry4.0 (I4.0) technologies in various related events. First, the paper presents a systematic literature review on the role of I4.0 technologies in SWM activities regarding waste types, waste management processes, and 5R strategies. Then, existing solutions supporting SWM4.0 are extracted to develop a framework for exploring the use of I4.0 technologies. This framework includes sharing the four main pillars that contribute to the success of SWM4.0, namely smart people, smart cities, smart enterprises, and smart factories. Furthermore, this review suggests the possibility of unifying and extending existing solutions and identifying the necessary links and interfaces for researchers. For managerial implications, the framework identifies future strategies to fulfill specific SWM tasks and to foster new technological solutions for future research.
... The accurate site model not only allows for the simulation of potential energy generation with RES, but can also link generation with energy demand in the district, forming a single holistic system. Kohne et al. [29] conducted a case study on a DT model for an industrial heat transfer station connecting industrial heat networks with DH systems. The aim was to establish an effective integration of low-exergy waste heat from the production processes. ...
... The aim was to establish an effective integration of low-exergy waste heat from the production processes. The case study at an industrial site showed a significant potential for waste heat recovery, reaching up to 70% and leading to a substantial reduction in the operating costs of up to 6% [29]. ...
The building sector accounts for over 40% of global energy consumption, and many buildings are old and inefficient. However, the current pace of building renovation is not sufficient to make a tangible impact. A new strategy is needed to accelerate the renovation process. Renovation at the district level and the use of digital tools, such as a digital twin (DT) of a city district, can provide a solution. This paper proposes a novel approach to city block renovation using renewable energy sources (RES), including photovoltaic (PV) solar panels, heat pumps (HP), and electric heaters (EH), while utilizing a DT of a city district to provide a user-friendly representation of the results and data needed for holistic solutions. The proposed method combines an optimization model of the optimal heating system with a solar PV simulation technique to analyse hybrid RES solutions and potential on-site energy generation and supply. Several scenarios are simulated to evaluate RES solutions in the renovation process of the city block using the DT concept. The simulation results demonstrate that a hybrid RES solution, which includes a PV system and a heating system, is optimal when the on-site generated energy is used not only for domestic electricity consumption, but also for the operation of HPs and EHs for heat generation. This study highlights the importance and significance of a DT approach to city block renovation and provides a new solution to accelerate the renovation process and reduce energy consumption in the building sector.
... The waste heat utilization rate is as high as 70%, and the operating cost is reduced by 6%. 49 ...
In order to improve the safety performance of the complete industrial chain, the petrochemical industry is in urgent need of digitalization and modernization. Considering two types of safety issues, namely safety hazards originating from non‐human and human sources, this paper proposes two solutions. Digital twin technology provides an effective way for the integration of the physical and information worlds through bidirectional real mapping and real‐time interaction between physical and virtual production lines, which can meet the high requirements of safety, stability, and continuity in the production process. On the other hand, micro‐ergonomics improves human–machine interaction, enhances comfort and work efficiency, and reduces negative emotions and the prevalence of musculoskeletal disorders; macro‐ergonomics and health, safety, environment, and ergonomics (HSEE) concepts take into account the human factor to maximize work efficiency and reduce the rate of injuries, morbidity, and accidents at work.
... Other examples include DTs for decision making in energy system design [32] and, during operation, the application of DTs for wind turbines [33] and for scheduling [34] and state estimation [35], to name just a few. Only recently, the transfer of the DT concept to thermal IES has begun [36,37]. For a complete overview of existing DT research and industrial application, we refer interested readers to the current literature review by Liu et al. [18]. ...
Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector.
Digitalization is crucial for achieving product quality, cost reduction, and timely delivery in manufacturing. With the rise of Industry 4.0, Digital Twin technology has gained popularity, enabling real-time monitoring, predictive maintenance, process optimization, and assumption testing. Manufacturing Digital Twins utilize digital models to represent physical processes, facilitating continuous simulation, interaction, correction, and optimization for autonomous control. However, implementing Digital Twins can be challenging due to diverse data requirements and multiple services. This paper proposes a scalable, service-oriented multi-layered architecture for developing Digital Twins in mineral processing, which can be adapted to other manufacturing systems. The paper provides an overview of Digital Twins, their data-centric workflow, and industrial applications. Additionally, a case study on implementing a Digital Twin for the froth flotation process in the mining industry is presented based on the proposed architecture.
In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies.
Digitalization in mining industry has become animportant component nowadays in order to improve theproductivity, the efficiency while increasing the availability ofmachines and different equipment The use of digital twin as afeature in mining industry has shown promising results to dealwith its multiple challenges such as maintenance, production,energy consumption. In this paper, a comparative study ispresented of developed digital twins in different applicationssuch as energy management, manufacturing, industrial andcampus, the goal is to design a conceptual architecture of aproposed digital twin within presenting a case study of a stackermachine in the experimental open pit mine of Benguerir. Thisdesign presented by 4 layers : physical, Data pre-processing,Edge computing, and cloud data, the goal is to work oninteractions between these layers to enhance the performance ofdifferent mining machines. The case study elucidates a practicalresult on Stacker machine used in mining industry simulatingthe mechanism and the automatism of this system. Thisdeveloped digital twin will be used to simulate critical scenariosand see the behavior of the stacker components in order tooptimize the usage of this machine increasing its availability andreliability.
Since the share of renewable energy continues to grow, the resulting fluctuations of energy supply in the electricity grid must be balanced. Electricity consumers can help achieving this by reacting to fluctuations in supply and adjusting their demand (demand response). As one of the largest electricity consumers, industry should embrace this opportunity by implementing energy flexibility measures for demand response. In an energy-flexible production, machines have multiple objectives: First they must meet the traditional production targets: high quality, low cost, and short production time. Additionally, they should react to electricity price signals or to external power change requests and adjust their electrical consumption, for example by interrupting an active process. Forecasts of future machine behavior including energy consumption and the state of machine components help to improve the effectiveness of a proposed energy-flexible controller that enables machines to achieve these objectives. In addition, the machine and the controller must communicate with each other and external sources. Therefore, we propose the implementation of a cyber-physical production system (CPPS) for energy-flexible operation of production machines. Our CPPS consists of a simulation model for forecasting, an automation data model for controlling production machines via OPC UA and of a software framework, which is based on co-simulation, to provide the environment for controlling and optimizing the production machine in an energy-flexible manner. We show that co-simulation can be used to achieve energy-flexible operation of production machines and avoid unsafe states of the system. We apply the framework to an aqueous component cleaning machine performing a batch process. The operation of the electric tank heating element and the start times of cleaning process steps are optimized. With this work we show the successful development of the CPPS and the corresponding software framework, which will be transferred to other machines and more complex controllers in the future.
Industrial cooling supply systems represent an essential component of many industrial production systems. Since these systems are responsible for a significant share of the final energy consumption of the industrial sector, the successful implementation of suitable energy efficiency measures can not only save costs, but also significantly reduce the CO2 emissions associated with the provision of energy. The planning and implementation of the required measures is often assisted by energy consultancies. In the potential analysis phase, existing or planned cooling supply systems are analysed and mapped in various degrees of detail to determine efficiency and flexibility potentials. To determine these potentials, temperature influences, partial machine load behaviours and waste heat utilizations have to be taken into account. Due to their high complexity, the energy and cost-related analyses are usually very time-consuming.
To support energy departments and consultancies regarding such analyses, we developed a Visual Basic for Applications (VBA)-based approach and programme that can be used to compile and analyse general industrial cooling supply systems in a modular and library-oriented way. Considering time-dependent framework conditions, respective machine loads and occurring heat flows, the approach integrates predefined main characteristics (e.g., the overall system efficiency, CO2 emissions or system costs) for each configuration. Furthermore, the programme offers the possibility to compare several configurations based on user-defined criteria such as total cost or CO2 emissions and thus, to determine the most cost- or energy-efficient configuration. Within this study, we applied our approach to data of an industrial use case and analysed alternative configurations to minimize cost and CO2 emissions by maximizing the overall system efficiency and to initiate more in-depth analyses by energy departments and consultancies in a targeted manner.
Companies and nations are developing strategies to reduce CO2 emissions and achieve their climate goals. Due to their high share of final energy demand, heating and cooling supply systems are of crucial importance regarding emission reduction. Here, especially the industrial and building sector have a dominating influence, accounting for 34% and 33% of the global final energy demand. Aside from reducing the energy demand or integrating renewable energy sources, the utilization of industrial waste heat represents an effective approach to minimize CO2 emissions in heating supply systems. Here, the coupling of the building and industrial sector via district heating systems offers the potential to utilize industrial waste heat. As the utilization is highly dependent on temperature levels and temporal occurrence, modeling approaches to evaluate the site-specific CO2 emission reduction potential must be developed. To consider heterogeneous industrial heating and cooling supply systems as well as the dynamic occurrence of industrial waste heat and energy demands, we present a tailored modeling approach. The approach enables to model configurable industrial heating and cooling supply systems which can be coupled to district heating systems via an industrial heat transfer station. The simulation models consider different energy converters, temperature levels, as well as waste heat sources. Within our research, different configurations are tested through a simulation study and evaluated by an integrated environmental and economic evaluation module. The simulation study, based on industrial thermal load profiles, shows a maximum waste heat utilization of 66% and a corresponding CO2 emission reduction of 39%, with regard to the industrial site’s emissions. The coupling with a district heating system can reduce the industrial site’s operating expenses by up to 14 %. However, the allocation of the overall economic benefit is dependent on the assumed heat price.
Nations and companies are forced to reduce CO2 emissions and decelerate global warming. In this development, the transition of the heating sector is still in its infancy despite the relatively large share of thermal energy in the total energy consumption. Industrial companies can contribute significantly to reduce CO2 emissions by using waste heat through connecting their industrial energy supply system (IESS) to a district heating system (DHS). This paper focuses on emission reduction potential of an (industrial) heat transfer station (HTS) regarding energy flexibility and sector coupling required for the successful integration of industrial waste heat. To optimize the operating behaviour of the HTS, a data and optimization model is integrated into a digital twin (DT) based on reference architecture model for industry 4.0 (RAMI4.0). Within the DT, the information, functional and business layer are modeled. The effects of operating the HTS supported by central modules of the DT are evaluated on one year’s data of an IESS of a real industrial site. The results show a potential operating cost reduction by 6 % for the IESS and increases in profits of 1.3 % for the DHS. Scope 2 emissions can be reduced by 25 % for the IESS and 180 % for the DHS respectively, strongly depending on emission factors and allocation methods.
Approximately 22 % of the total German energy consumption is used to provide thermal energy in the industrial sector. Therefore, energy efficiency measures in this area offer a major lever for decarbonizing the energy system. Central heating networks can achieve high energy efficiency levels through economies of scale, the coupling of efficient technologies, or the integration of waste heat. Although energy efficiency of an industrial heating network is a key aspect to minimize operating cost, heating networks are mostly planned and realized with fixed control parameters (e.g., pressure, flow rate or flow temperature). An adjustment of control parameters especially in changing production systems and the integration of renewable energies or waste heat often holds immense energy efficiency potential. This potential can only be harnessed by achieving transparency in the operation of heating networks outlining inefficient behavior.
This paper presents a method to continuously evaluate heat flows in industrial heating networks by deriving key performance indicators. The method focuses on linking heat demands as well as flow and return temperatures of production processes or building services to the CO2 emissions and energy efficiency of the heating network. The indicators include network and converter specific parameters such as part load behavior and temperature dependencies. To understand the effects on network efficiency in case of parameter changes (control parameters or network topology), both the generation and the demand side are included in the analysis. By that, operating behavior of an industrial heating network can be improved, e.g., by the integration of waste heat or the linkage to a district heating network. The method is conducted on real data of a complex heating network in an industrial site. An application for the reduction of return temperatures of a consumer showed that potentially 7.6 % of the total CO2 emissions could be reduced by the integration of waste heat.
Both rising and more volatile energy prices are strong incentives for manufacturing companies to become more energy-efficient and flexible. A promising approach is the intelligent control of Industrial Energy Supply Systems (IESS), which provide various energy services to industrial production facilities and machines. Due to the high complexity of such systems widespread conventional control approaches often lead to suboptimal operating behavior and limited flexibility. Rising digitization in industrial production sites offers the opportunity to implement new advanced control algorithms e. g. based on Mixed Integer Linear Programming (MILP) or Deep Reinforcement Learning (DRL) to optimize the operational strategies of IESS.This paper presents a comparative study of different controllers for optimized operation strategies. For this purpose, a framework is used that allows for a standardized comparison of rule-, model- and data-based controllers by connecting them to dynamic simulation models of IESS of varying complexity. The results indicate that controllers based on DRL and MILP have a huge potential to reduce energy-related cost of up to 50% for less complex and around 6% for more complex systems. In some cases however, both algorithms still show unfavorable operating behavior in terms of non-direct costs such as temperature and switching restrictions, depending on the complexity and general conditions of the systems.
The electric energy demand of data centers in Germany has grown rapidly from 10.5 TWh/a in 2010 to 13.2 TWh/a in 2017, an average of 25% of which are used to fulfill the data centers' cooling demand. In order to increase its energy efficiency, TU Darmstadt applies a new cooling concept in the next generation of its high‐performance computing data center “Lichtenberg II.” Instead of the current air‐cooled servers with water‐cooled rear doors at 17‐24°C, the new data center will be equipped with direct hot‐water cooling for the high‐performance computer, supplying heat at a temperature of 45°C. The high‐temperature waste heat is used for heating purposes on the university's campus Lichtwiese. For waste heat utilization, two concepts are presented, either integrating the heat in the return line of the district heating network or using it locally in buildings located near the data center. Reductions in CO2 emission and annuity are generated both by decreased compression cooling demand for the data center and by decreased heat generation due to waste heat utilization. Depending on the scenario, a total of 20%‐50% of the waste heat emitted by the high‐performance computer can be used for heating purposes, while the remaining heat is dissipated efficiently via free cooling without additional energy demand for mechanical chillers. CO2 emission can be decreased by up to 720 tCO2/a, representing a reduction of about 4% of the total emission at campus Lichtwiese. TU Darmstadt is currently implementing the waste heat integration into its district heating network and will benefit from this concept starting in 2020.
The role of a Digital Twin is increasingly discussed within the context of Cyber-Physical Production Systems. Accordingly, various architectures for the realization of Digital Twin use cases are conceptualized. There lacks, however, a clear, encompassing architecture covering necessary components of a Digital Twin to realize various use cases in an intelligent automation system.
In this contribution, the added value of a Digital Twin in an intelligent automation system is highlighted and various existing definitions and architectures of the Digital Twin are discussed. Flowingly, an architecture for a Digital Twin and an architecture for an Intelligent Digital Twin and their required components are proposed, with which use cases such as plug and produce, self-x and predictive maintenance are enabled.
In the opinion of the authors, a Digital Twin requires three main characteristics: synchronization with the real asset, active data acquisition from the real environment and the ability of simulation. In addition to all the characteristics of a Digital Twin, an Intelligent Digital Twin must also include the characteristics of Artificial Intelligence. The Intelligent Digital Twin can be used for the realization of the autonomous Cyber-Physical Production Systems.
In order to realize the proposed architecture for a Digital Twin, several methods, namely the Anchor-Point-Method, a method for heterogeneous data acquisition and data integration as well as an agent-based method for the development of a co-simulation between Digital Twins were implemented and evaluated.
As the industrial requirements change at a rapid pace due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient manufacturing system design arises more intensely than ever. Manufacturing systems simulation has proven to be a powerful tool for designing and evaluating a manufacturing system due to its low cost, quick analysis, low risk and meaningful insight that it may provide, improving thus the understanding of the influence of each component. Simulation comprises an indispensable set of IT tools and methods for the successful implementation of digital manufacturing. It allows experimentation and validation of product, process, and system design and configuration. This paper investigates the major historical milestones in the evolution of manufacturing systems simulation technologies and examines recent industrial and research approaches in key fields of manufacturing. It describes how the urge towards digitalisation of manufacturing in the context of the 4th Industrial revolution has shaped simulation in the design and operation of manufacturing systems and reviews the new approaches that have arisen in the literature. Particular focus is given to technologies in the digitalised factories of the future that are gaining ground in industrial applications simulation, offering multiple advantages.
This article investigates 40 thermal networks in operation in Europe that are able to cover both the heating and cooling demands of buildings by means of distributed heat pumps installed at the customer substations. The technology of thermal networks that work at a temperature close to the ground, can strongly contribute to the decarbonisation of the heating and cooling sector and furthermore exploit a multitude of low temperature heat sources. Nevertheless, the nomenclature used in literature shows that misinterpretations could easily result when comparing the different concepts of thermal networks that operate at a temperature level lower than traditional district heating. The scope of this work is to revise the definitions encountered and to introduce an unambiguous definition of Fifth-Generation District Heating and Cooling networks. A drawback-benefit analysis is presented to identify the pros and cons of such technology. The survey on the current networks shows that on average three Fifth-Generation District Heating and Cooling systems per year have entered the heating and cooling market in the last decade. Pioneer countries in such technology are Germany and Switzerland. For some networks, the assessed Linear Heating Power Demand Density results are lower than the feasibility threshold adopted in traditional district heating. High performances and low non-renewable primary energy factors are achieved in systems that exploit a very high share of renewable or urban excess heat sources. With respect to traditional district heating, the surveyed pumping energy consumptions result one order of magnitude higher, whereas the implemented control strategies can be completely different, leading the network temperature to float freely.
Industrial waste heat is examined in EU countries, focusing on the amount that can be recovered and exploited, referred to as technical potential of waste heat. An alternative methodology is proposed here, which is based on waste heat fractions derived from a detailed study of the UK industry from the period 2000–2003. These fractions express the part of heat consumption that is wasted and is possible to be recovered. The waste heat fractions have been calculated in this work for each main industrial sector and temperature level. The methodology initially includes the adjustment of waste heat fractions from each industrial sector from the UK industry to the conditions of the different EU countries in the period 2000–2003, in order to account for the different levels of energy efficiency. The second step is to adjust the fractions for the year 2015, using data about the evolution of energy intensity values from 2000 to 2003 to 2015 for each country and sector, resulting to a new set of fractions per country, temperature level and sector.
This methodology has enabled the authors to study in detail the waste heat potential per sector and temperature level, using the most recent data. The main outcome is the estimation of waste heat potential for each main industrial sector in the EU, broken down to the amount of waste heat for each temperature range. A similar analysis is conducted for each EU country as well, in order to identify the magnitude of heat recovery opportunities that could exist for every industrial sector at country level.
The main result of this analysis is the estimation of the total waste heat potential in EU, which is about 300 TWh/year, with one third corresponding to temperature level below 200 °C, which is often referred to as low-temperature waste heat, another 25% in the range 200–500 °C and the rest above 500 °C (mostly in the range 500–1000 °C).
The findings of the current study can be used for assessing the potential of any relevant heat recovery applications, such as heat upgrade and re-use or heat-to-power conversion technologies.
Open access available: https://www.sciencedirect.com/science/article/pii/S1359431117347919
The integration of decentralized feed-in of solar heat into district heating networks results in a high volatility regarding daily and seasonal pressure and temperature characteristics. Therefore new control and automation devices and algorithms are required to handle the complexity of the system. Furthermore the presented combined supply and feed-in substation – a so-called prosumer substation – needs a state machine (automation of switching between the several operation modes) in order to optimise the distribution of solar heat gains: feed-in into the district heating network and/or partly to local consumption. This paper presents results regarding the developed controller system, the test rig for emulation of the solar thermal system and a building as well as measurement data of a complex part of chosen daily load curves. The latter is the synthesis of the emulation test rig, the functional model of the combined supply and feed-in substation and the control system. The presented results are gained as parts of the R&D projects “Dezentrale Einspeisung in Nah- und Fernwärmesysteme unter besonderer Berücksichtigung der Solarthermie” and “Kostenreduktionspotential beim Ausbau der Solarisierung von Fernwärmenetzen durch Standardisierung” (see [1] and [2],[3]).
This paper presents the results of a multi-criteria evaluation of different design scenarios for a micro-DH network in Vienna, Austria, which involves energy efficient buildings (supplied by heat-pumps and solar thermal panels) and a standard building (equipped with gas boilers). The aim is to assess the integration of industrial waste heat available on-site together with high and low-temperature storages, to balance the heat production and demand, and a heat-pump booster, to supply the network with the required temperature level. The integration of different temperature levels and different profiles from both the production side and consumption side constitute the main challenges.
The paper presents the design of a smart thermal grid in the Wilhelmsburg district of Hamburg. It is emphasized how technical and economical concepts are cross-linked and how district heating systems can become more flexible and efficient by focusing on the whole lifecycle process from customer acquisition over implementation up to operation. Technical concepts for centralized and decentralized generation, grid design and consumer integration are shown. Currently some of these are in the process of implementation, e.g. a grid coupling point, power house expansion, and flexible customers using 3-pipe connections and smart substations.
Global Warming of 1.5°C.An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways
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Heat Roadmap Germany: Quantifying the Impact of Low-Carbon Heating and Cooling Roadmaps
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