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Test Platform and Component Model for Modular Sorption Heat Pumps
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (Reesbe)ORCID iD: 0000-0002-1203-3016
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Increasing the share of renewable sources of energy as well as the effective use of waste energy sources is critical to the reduction of primary energy use and its associated environmental impact in the built environment. Sorption heat pumps are employed in various heat-driven cooling and heat pumping applications. These heat pumps may be driven by solar energy, natural gas, biogas, geothermal energy or waste heat. Given that a plethora of heat sources and sorption materials can be exploited for different applications, various sorption heat pump modules have been developed. The sorption modules are pre-engineered sorption components for increased ease of sorption system development, improved cost effectiveness and reduced system complexity for various applications. However, in the design of sorption modules, component and system modelling and simulation are useful in the process of determining the optimal candidate of several possible sorption working couples for a given application. A test platform has been developed and a test strategy devised for the rapid characterisation of the transient behaviour of the sorption modules. In the present study, a modular sorption unit is evaluated experimentally in an automated test setup. Key performance indicators were derived, and the test data used as input to train a model based on artificial neural networks (ANN) in MATLAB. The study showed that the model could adequately predict the dynamic behaviour of the sorption module. Results showed that the dynamic behaviour of the module could be adequately mapped, with average relative errors between measured and simulation results of 3.7%, 4.2%, 0.4%, and 0.3% for heat transfer rates to and from the reactor, and the condenser-evaporator during charge and discharge respectively. Additionally, the ANN model, trained with data from test run sequence of 54 cycles, predicted both cooling and heating COPs within a reasonable margin of error (<± 8%) with the majority of predictions having an error of less than ± 4%.

Keywords [en]
Sorption, Artificial Neural Network, Heat Pump, Sorption Module
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-49196OAI: oai:DiVA.org:mdh-49196DiVA, id: diva2:1448610
Available from: 2020-06-29 Created: 2020-06-29 Last updated: 2022-11-09Bibliographically approved
In thesis
1. Evaluation of Modular Thermally Driven Heat Pump Systems
Open this publication in new window or tab >>Evaluation of Modular Thermally Driven Heat Pump Systems
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The building sector accounts for approximately 40% of primary energy use within the European Union, therefore reductions in the energy use intensity of this sector are critical in decreasing total energy usage. Given that the majority of energy used within the built environment is for space conditioning and domestic hot water preparation, prudence would suggest that decreasing primary energy used for these end purposes would have the biggest overall environmental impact. A significant portion of the energy demands in buildings throughout the year could potentially be met using solar energy technology for both heating and cooling. Additionally, improving the efficiency of current heating and cooling appliances can reduce environmental impacts during the transition from non-renewable to renewable sources of energy. However, in spite of favourable energy saving prospects, major energy efficiency improvements as well as solar heating and cooling technology are still somewhat underutilised. This is typically due to higher initial costs, and lack of knowledge of system implementation and expected performance.

 

The central premise of this thesis is that modular thermally (i.e., sorption) driven heat pumps can be integrated into heating and cooling systems to provide energy cost savings. These sorption modules, by virtue of their design, could be integrated directly into a solar thermal collector. With the resulting sorption integrated collectors, cost-effective pre-engineered solar heating and cooling system kits can be developed. Sorption modules could also be employed to improve the efficiency of natural gas driven boilers. These modules would effectively transform standard condensing boilers into high efficiency gas-driven heat pumps that, similar to electric heat pumps, make use of air or ground-source heat.

 

Based on the studies carried, sorption modules are promising for integration into heating and cooling systems for the built environment generating appreciable energy and cost-savings. Simulations yielded an annual solar fraction of 42% and potential cost savings of €386 per annum for a sorption integrated solar heating and cooling installation versus a state-of-the-art heating and cooling system. Additionally, a sorption integrated gas-fired condensing boiler yielded annual energy savings of up to 14.4% and corresponding annual energy cost savings of up to €196 compared to a standard condensing boiler.

 

A further evaluation method for sorption modules, saw the use of an artificial neural network (ANN) to characterise and predict the performance of the sorption module under various operating conditions. This generic, application agnostic model, could characterise sorption module performance within a ± 8% margin of error. This study thus culminates in the proposal of an overall systematic evaluation method for sorption modules that could be employed for various applications based on the analytical, experimental and simulation methods developed.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2020
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 316
Keywords
sorption heat pump, sorption module, thermochemical energy storage, artificial neural network, built environment, solar energy, gas-driven heat pump, solar cooling, heating and cooling, renewable energy, energy efficiency, experimental, simulation, analytical
National Category
Engineering and Technology
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-49197 (URN)978-91-7485-472-5 (ISBN)
Public defence
2020-09-08, Dalarna University, Borlänge, 09:15 (English)
Opponent
Supervisors
Available from: 2020-06-30 Created: 2020-06-29 Last updated: 2022-11-08Bibliographically approved

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