Browsing by Author "Laukkanen, Timo, Dr., Aalto University, Finland"

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  • Long-term thermal energy storage with cold-crystallizing materials - Method, properties and scale-up
    (2023) Turunen, Konsta
     School of Engineering | Doctoral dissertation (article-based)
    Thermal energy storage (TES) is an attractive technology for balancing the variations in renewable energy production because currently, half of the global final energy consumption consists of heating, which mainly relies on fossil fuels. If an efficient and compact long-term TES emerged, viability of the renewable energy production would improve as seasonal variations could be smoothened. One way to achieve long-term TES is to utilize supercooling, glass transition and cold-crystallization to store and release the latent heat of melting. This work provides new knowledge on crystallization behaviour of cold-crystallizing materials and their implementation in TES applications. This thesis categorized mixtures of sugar alcohols and polymers, and their crystallization, thermal and morphological characteristics. Additionally, the key storage parameters of potential compositions were determined using a TES prototype system and a thermal chamber measurement procedure. The results reveal that the crystallization mechanism changes below 1.2*Tg (Tg= glass transition temperature, (K)) and the crystallization kinetics drastically reduce below 1.14*Tg. Additionally, reducing the lowest temperature achieved during supercooling accelerated the subsequent cold-crystallization at a constant temperature. The observed crystallization behaviour was explained in terms of energy landscape of the material and conformational flexibility of the sugar alcohol. The material showing the highest potential for long-term storage applications possessed a volumetric melting enthalpy of 200 MJ/m3, which is in the mid-range of typical phase change materials used in TES. Moreover, it demonstrated high storage efficiency after a nine-month storage at 10 °C. However, the materials should be used in a combined short- and long-term storage to yield high round-trip efficiency of 0.50-0.80, which depends considerably on the temperature at which the released heat is used. This work explains and demonstrates experimentally the fundamental changes in the crystallization behaviour occurring below 1.2*Tg, which enables using the supercooling, glass transition and cold-crystallization methods for long-term storing and adequate release rate of thermal energy. Furthermore, the results confirmed that this method may be practically applied to TES systems, indicating that advanced material solutions have potential to replace fossil fuel heating sources.
  • Parametric Models for Forest Industry Transformation in Energy Efficiency: Machine Learning Approach
    (2023) Talebjedi, Behnam
     School of Engineering | Doctoral dissertation (article-based)
    This thesis is based on industrial projects with Pulp and Paper industry in a Nordic country. The main focus of the thesis is on the energy efficiency development of the thermomechanical pulp (TMP) mill and optimal integration of the TMP mill and paper machine through heat recovery and the concept of an Energy Hub. Advanced statistical approaches and machine learning methods have been employed to develop refining identification models and advanced energy-saving refining optimization methods for the TMP process. Results prove that an accurate refining identification model could be developed through advanced machine learning methods. The refining identification models to predict the refining energy (such as specific energy consumption) and final pulp quality (such as freeness and fiber length) can be further used to develop a refining control and optimization strategy. The developed optimization strategy based on the integration of Machine learning methods and Genetic optimization algorithm confirms an average reduction of 14 % for the total refining-specific energy consumption. In the following, the optimal integration of the TMP mill and paper machine has been investigated through the Energy Hub (EH) concept. The proposed approach for the cost and energy-efficient design and operation of EH is based on the integration of thermo-economic analysis, reliability and availability analysis, and EH load prediction. The proposed approach was first introduced and evaluated for the energy and cost-efficient design of a combined cooling, heating, and power (CCHP) system that provides the hourly thermal demand of a high-rise residential building. Results prove that by utilizing the proposed method, the system's average total cost could be reduced by 16% during the system's lifespan. As the presented method has shown to be effective in residential EH applications, this method was examined in a second case study (the forest industry) to determine the optimal integration of TMP mill and paper machines. The proposed design method offers a robust design that isn't impacted by penalty rates of unsupplied demand. Depending on the penalty rates, the total system cost could decrease by 14%-28% utilizing the proposed design method.