Mechanism Study of Waste Heat Recovery from Slurry by Surface Scraped Heat Exchanger

Chen, Jingjing; Ma, Chuanyan; Ji, Xiaoyan; Lü, Xiaohua; Wang, Changsong

doi:10.1016/j.egypro.2017.03.474

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…The values obtained for A k , E k , A n and E n are 3.5754 × 10 −4 , 4578.4, 4.4965, and −602.16, respectively. These values are in the same order of magnitude of the values reported for other fluids with slimming behavior, such as ice cream mixture [37].…”

Section: Estimation Of Apparent Viscosity From Temperature (Cooling Ssupporting

confidence: 83%

Estimation of Ice Cream Mixture Viscosity during Batch Crystallization in a Scraped Surface Heat Exchanger

et al. 2020

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Ice cream viscosity is one of the properties that most changes during crystallization in scraped surface heat exchangers (SSHE), and its online measurement is not easy. Its estimation is necessary through variables that are easy to measure. The temperature and power of the stirring motor of the SSHE turn out to be this type of variable and are closely related to the viscosity. Therefore, a mathematical model based on these variables proved to be feasible. The development of this mathematical relationship involved the rheological study of the ice cream base, as well as the application of a method for its in situ melting in the rheometer as a function of the temperature, and the application of a mathematical model correlating the SSHE stirring power and the ice cream viscosity. The result was a coupled model based on both the temperature and stirring power of the SSHE, which allowed for online viscosity estimation with errors below 10% for crystallized systems with a 30% ice fraction at the exit of the SSHE. The model obtained is a first step in the search for control strategies for crystallization in SSHE.

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Section: Estimation Of Apparent Viscosity From Temperature (Cooling Ssupporting

confidence: 83%

Estimation of Ice Cream Mixture Viscosity during Batch Crystallization in a Scraped Surface Heat Exchanger

et al. 2020

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“…This value can be determined based on the Formula (19). (19) Using Formula (20), a reduction in the cost of obtaining heat O for heating hot water was calculated for 1 kWh in relation to the cost of electricity.…”

Section: Methodsmentioning

confidence: 99%

An Analysis of Waste Heat Recovery from Wastewater on Livestock and Agriculture Farms

2020

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Agriculture is one of the sectors of the economy in which it is possible to conduct much more rational energy economy. The easiest way to achieve financial savings as well as reduce air pollution is to use waste heat sources. Heat pumps are perfect for this. Particularly favorable is the case when the device can operate in an alternative system and serve both heating and cooling purposes. The purpose of this article was to present possible solutions for installations enabling heat recovery from wastewater to supply agri-breeding farms with hot utility and technological water, a financial analysis of their application, and an assessment of the impact of these solutions on possible reduction of pollutant emissions. The tests were carried out for four variants of cooperation between a heat pump and an exchanger. In the first variant, waste heat was used in the process of heating water used to clean stands and prepare feed. In the second variant, waste heat took part in heating the water used for watering plants. In the third variant, waste heat was used in the process of drying cereals. In turn, in the last variant, waste heat supported the preparation of utility hot water for the breeder’s residential building. The study showed the legitimacy of using thermal energy from liquid manure as a waste heat source on farms and farming. This is mainly due to the short payback period, which can be within 2–4 years. In turn, the analysis of pollution reduction associated with the recovery of waste energy showed that the use of heat pumps allowed a significant reduction in the emission of harmful compounds to the atmosphere, in particular carbon dioxide. It is worth noting that livestock breeding is one of the most important branches of agricultural production not only in Poland but also throughout Europe, Asia and South and North America. For this reason, the use of waste heat-recovery systems enables real savings in the purchase of energy and reduction of pollutant emissions arising during traditional production processes.

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“…However, it comes with a high complexity, and modeling complex systems using this approach may encounter limitations. Another approach involves modeling based on a large amount of actual operational data [12], utilizing statistical and data analysis techniques to establish mathematical models through data fitting. Cao [13] employed a data-driven approach to modeling, addressing issues related to low data quality.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of Heat Exchangers Based on Mechanism and Reinforcement Learning Synergy

Sun,

Jia,

Zhao

et al. 2024

Buildings

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The current lack of a high-precision, real-time model applicable to the control optimization process of heat exchange systems, especially the difficulty in determining the overall heat transfer coefficient K of heat exchanger operating parameters in real time, is a prominent issue. This paper mainly unfolds the following work: 1. We propose a dynamic model for the control and optimization of the heat exchanger operation. By constructing a system to collect real-time operating data on the flow rates and temperatures on both sides of the heat exchanger, the parameter identification of the overall heat transfer coefficient K is performed. Subsequently, by combining this with mechanistic equations, a novel heat exchanger model is established based on the fusion of mechanistic principles and reinforcement learning. 2. We validate the new model, where the average relative error between the model’s temperature output values and the actual measured values is below 5%, indicating the high identification accuracy of the model. Moreover, under variations in the temperature and flow rate, the overall heat transfer coefficient K demonstrates the correct patterns of change. 3. To further enhance the model’s identification accuracy, a study on the reward functions in reinforcement learning is conducted. A model with the Logarithmic Mean Temperature Difference (LMTD) as the reward function exhibits a high identification accuracy. However, upon comparison, a model using the Arithmetic Mean Temperature Difference (AMTD) for relative error as the reward function shows an even higher identification accuracy. The model is validated under various operating conditions, such as changes in the flow rate on the hot side, demonstrating good scalability and applicability. This research contributes to providing a high-precision dynamic parameter basis for the precise control of heat exchange systems, offering significant guidance for the control optimization of actual heat exchange system operations.

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Mechanism Study of Waste Heat Recovery from Slurry by Surface Scraped Heat Exchanger

Cited by 7 publications

References 17 publications

Estimation of Ice Cream Mixture Viscosity during Batch Crystallization in a Scraped Surface Heat Exchanger

Estimation of Ice Cream Mixture Viscosity during Batch Crystallization in a Scraped Surface Heat Exchanger

An Analysis of Waste Heat Recovery from Wastewater on Livestock and Agriculture Farms

Dynamic Modeling of Heat Exchangers Based on Mechanism and Reinforcement Learning Synergy

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