Preprint Article Version 1 This version is not peer-reviewed

Heat Pump Dryer Design Optimization Algorithm

Version 1 : Received: 23 July 2019 / Approved: 25 July 2019 / Online: 25 July 2019 (06:35:55 CEST)

How to cite: Andrade, B.; Amorim, I.; Silva, M.; Savosh, L.; Frölén Ribeiro, L. Heat Pump Dryer Design Optimization Algorithm. Preprints 2019, 2019070281 (doi: 10.20944/preprints201907.0281.v1). Andrade, B.; Amorim, I.; Silva, M.; Savosh, L.; Frölén Ribeiro, L. Heat Pump Dryer Design Optimization Algorithm. Preprints 2019, 2019070281 (doi: 10.20944/preprints201907.0281.v1).

Abstract

Drying food involves complex physical atmospheric mechanisms with non-linear relations from the air-food interactions. Moreover, those relations are strongly dependent on the moisture contents and the actual type of food. Such dependence makes it complex to design suitable machines dedicated to a single drying process. To speed-up and streamline the drying machine design, a heat pump dryer design optimization algorithm was developed. The proposed algorithm inputs food and air proprieties, the volume of the drying container and the technical specifications of the heat-pump off-the shelf components. The heat required to dehumidify the food equals the heat exchange process from condenser to evaporator, and the compressor’s requirements (refrigerant mass flow rate and operating pressures) are then calculated. Compressors can then be select based in the volume and type of food to be dried. The algorithm is shown via a flow chart to guide the reader throughout 3 different stages representing each singular physical phenomenon: analysis of the internal air properties; heat flow analysis between components, air and food; food humidity calculus and verification. Results of the application of the algorithm are presented for the drying of Agaricus Blazei mushroom with 3 different humidity contents (60, 80 and 88% of water) for batches of about 45, 123, 200, 277 and 355 kilograms. The results indicate that for the first batch a 610 W compressor will suffice, while for the second one a 990 W compressor will deliver the required work to the refrigerant gas. Further, the last 3 ones would demand for a more potent 1445 W compressor.

Subject Areas

algorithm; heat-pump; drying; food; design; optimization

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