Organic Rankine cycle (ORC) is suitable to converting the normally hard to utilize low temperature thermal energies, such as geothermal energy, solar energy, and industrial waste heat, to electricity through utilizing low boiling organic working fluids. The performance of ORC system is dramatically affected by the selections of working fluid and working conditions. As a key component of waste heat recovery, the irreversible loss of evaporator also has great influence on the performance of ORC system. In this paper, we study the heat transfer performance in evaporator under the condition that the heat source parameters and pinch point temperature difference are identified. It is found that the heat transfer performance is affected by C r , the ratio of heat capacity flow rates between the working fluid and the heat source fluid. The equivalent thermal resistance, deducing from the concept of entransy, to measure the irreversability during the heat transfer process is used. Then, the parameter κ r , the ratio between latent heat and sensible heat of working fluid is defined. With the parameters C r and κ r , we investigate the relationship between the heat transfer and irreversible loss, and deduce the condition that maximum heat transfer and minimum equivalent thermal resistance occurs. Finally, a calculation method is established to choose the optimum working fluid and the evaporation condition.
To realize the efficient decomposition and allocation of collaborative production tasks and resources among multiple enterprises, a task decomposition and allocation model for collaborative production among multiple manufacturing enterprises is proposed in a big data environment. The model is designed for the efficient and fast processing of production information using big data technology. This study innovatively applies the 5S management method to conduct data preprocessing for a manufacturing service provider and design the operation process of data cleaning and conversion to improve the efficiency of data processing. A collaborative optimization model, based on a hierarchical model with seven levels and considering time, costs, and services, is established for the task of production to achieve a reasonable match between supply and demand. Finally, the correlation coefficients of manufacturing service providers are configured according to weight order, so that the weight order is symmetrical with that of the manufacturer. The model also engages all manufacturing service providers with different production capabilities in collaborative production. The model is proved to be scientific and effective by using a specific example. In cooperative production activities, the production tasks of small and medium-sized enterprises can be effectively allocated. It can also realize efficient cooperative production among multiple manufacturing enterprises.
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