INDOOR HUMAN THERMAL COMFORT OPTIMAL CONTROL WITH DESICCANT WHEEL COOLING SYSTEM

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1 Available online at ScienceDirect Energy Procedia 61 (2014 ) The 6 th International Conference on Applied Energy ICAE2014 INDOOR HUMAN THERMAL COMFORT OPTIMAL CONTROL WITH DESICCANT WHEEL COOLING SYSTEM Nan Wang a, Xiaohua Xia a a Centre of New Energy Systems, Department of Electrical, Electronic and Computer Engineering,University of Pretoria, Pretoria 0002, South Africa Abstract Human thermal comfort is an important concern in the energy management of commercial buildings. Human thermal comfort research focuses mostly on the temperature control or the humidity control while based on human thermal comfort index control is ignored. In this paper, an optimal human thermal comfort control model (OHTCM) for a desiccant wheel cooling system is presented for the dehumidification and cooling of a commercial building in summer seasons. The OHTCM has two objectives. The first objective of the OHTCM is to minimize the predicted percentage of dissatisfied (PPD) which is the human thermal comfort index, and the second objective is to minimize the power consumption of the desiccant wheel cooling system. Model predictive control (MPC) strategy has the ability to handle constraints, being able to use simple models and to change controls dynamically in terms of temperature, humidity and air velocity changes, which makes it very practical to use in indoor human thermal comfort control problem. Therefore, MPC strategy is applied to implement the optimal operation of the desiccant wheel cooling system during working hours of the commercial building. To illustrate the practical applications of the MPC strategy, the optimization of the desiccant wheel cooling system in a commercial building of South Africa is studied Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 2014 The Authors. Published by Elsevier Ltd. ( Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the Organizing Committee of ICAE2014 Keywords: Human thermal comfort index; Desiccant wheel; Model predictive control Area of duct [m 2 ] Temperature [ C] Specific heat of air [J kg -1 C -1 ] Temperature of inlet air of heater[ C] Moisture generation rate [kg s -1 ] Mass flow rate of water [kg s -1 ] Enthalpy of indoor air[kj kg -1 ] Velocity of air [m s -1 ] Initial enthalpy of indoor air[kj kg -1 ] Wheel speed [rph] Humidity ratio [kg kg -1 ] Activity level [W m -2 ] Clothing insulation [col] Mass of indoor air [kg] Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the Organizing Committee of ICAE2014 doi: /j.egypro

2 Nan Wang and Xiaohua Xia / Energy Procedia 61 ( 2014 ) Air flow rate [m 3 s -1 ] ε Desiccant wheel efficiency β Weight factor ρ Density of air [kg m -3 ] Time [s] Subscripts Outlet air of the dehumidification section Lower bound Inlet air of the dehumidification section Mean radiant Inlet air of the regeneration section Outdoor air Indoor air supply air Initial condition Upper bound Infiltration 1. Introduction Evidences indicate that indoor human thermal comfort is closely related to health problems and human productivity. Therefore, keeping indoor human thermal comfort steady at the correct level is very important to ensure people s health in commercial buildings. For the dehumidification and cooling of commercial buildings in summer, existing research focuses on developing new energy efficient equipment [1], applying different control strategy in traditional air conditioning [2], or changing design parameters of dehumidifier [3-7]. Model predictive control (MPC) strategy has the ability to handle constraints, being able to use simple models and to change controls dynamically in terms of system changes, which makes it very practical to use in various energy problems. [8] proposes an MPC approach to the periodic implementation of the optimal solutions of a class of resource allocation problems. [9] presents the MPC strategy applied to the temperature control of a building. None of the existing research applies MPC strategies in the desiccant wheel cooling system of commercial buildings to maintain a required indoor human thermal comfort. In this paper, an optimal human thermal comfort control model (OHTCM) for a commercial building with a desiccant wheel cooling system is presented. MPC strategy is applied to implement the optimal operation of the desiccant wheel cooling system during working hours of the commercial building. To illustrate the practical applications of the MPC strategy, the optimization of the desiccant wheel cooling system in a commercial building of South Africa is studied. The layout of the paper is as follows. In Section 2, the background of the desiccant wheel cooling system model is recalled. The formulation of OHTCM and the procedure of the MPC strategy can be found in Section 2 as well. Section 3 illustrates the results of MPC strategy by a case study. 2. Background The desiccant wheel cooling system to be considered in this paper is shown in Fig. 1. The desiccant wheel is driven by an engine and moves at a given rotary velocity. The desiccant wheel is divided into two sections: dehumidification section (position 1 to 2) and regeneration section (position 6 to 7). Water vapour of outdoor air is absorbed by the desiccant wheel when the outdoor air is moving from position 1 to 2. After the indoor hot air is heated up by the heat exchanger (position 4 to 5) and heater (position 5 to 6), the heated air is flowing through the desiccant wheel. Then the water is desorbed out from the desiccant and the desiccant material is regenerated (position 6 to 7). The dehumidified air is pre-cooled through the heat exchanger. The pre-cooled air is cooled by the cooling system when the pre-cooled air is flowing through the evaporator (position 2 to 3) and sent to the building.

3 884 Nan Wang and Xiaohua Xia / Energy Procedia 61 ( 2014 ) Figure 1 Typical desiccant wheel cooling system set-up The optimal human thermal comfort control model (OHTCM) of the desiccant wheel cooling system has two objectives. The first objective of the OHTCM is to minimize the predicted percentage of dissatisfied (PPD) which is the human thermal comfort index during working hours [10], and the second objective is to minimize the power consumption of the desiccant wheel cooling system during the same time period. Therefore, the OHTCM can be formulated as follows: + β + β =+ = + = = ε = + = + + ρ = = + + = β where is the cooling load of indoor environment, is the power consumption of cooling system, is the power consumption of whole desiccant wheel cooling system, and are the vector of outdoor humidity ratio and temperature during the -th period of nonworking hours respectively. The above OHTCM is a quadratic linear programming problem. It is solved by Matlab genetic algorithm (GA) toolbox in this paper. The population size of the GA method is set as 100, and the number of generations is chosen as Now the following MPC strategy is obtained. (1) Initialize the conditions of desiccant wheel cooling system and indoor environment at time instant and let = Measure the outdoor temperature and humidity ratio at time instant and solve the revised OHTCM over time interval+ + to find its optimal solution. The optimal solution of the revised OHTCM at time instant + is denoted by α + Implement α + to control the desiccant wheel cooling system, let = + and go to Step (2). 3. Results and Discussion

4 Nan Wang and Xiaohua Xia / Energy Procedia 61 ( 2014 ) Consider a commercial building in South Africa, the period of daily working hours is from 8:00 to 18:00. The simulation results are shown in the Fig. 2 and 3. The variation of PPD during working hours of the two days is shown in Fig. 2. Figure 2 Variation of PPD during working hours of the two days The blue line shows the variation of PPD under optimal human thermal comfort control. The red line shows the variation of PPD under MPC strategy with constant temperature set point. As shown in Fig. 2, the maximal value of PPD under optimal human thermal comfort control is 9.71% and the minimal value of PPD is 5.22%. Fig. 3 shows the variation of power consumption during working hours of the two days. Figure 3 Variation of power consumption during working hours of the two days The blue line shows the variation of power consumption of desiccant wheel cooling system under optimal human thermal comfort control. The red line shows the variation of power consumption of desiccant wheel cooling system under MPC strategy with constant temperature set point. As shown in Fig. 3, the power consumption of desiccant wheel cooling system can be reduced while the optimal human thermal comfort control is implemented. References [1] Mazzei P, Minichiello F, Palma D. HVAC dehumidification systems for thermal comfort: a critical review. Applied Thermal Engineering 2005; 25: [] Chua KJ, Chou SK, Ho JC. A model to study the effects of different control strategies on space humidity during part-load conditions. Building and Environment 2008; 43: [] Zahra H, Mohammad HS. Optimization of solar collector surface in solar desiccant wheel cycle. Energy and Buildings 2012; 45: [] Subramanyam N, Maiya MP, Murthy S. Application of desiccant wheel to control humidity in air-conditioning systems. Applied Thermal Engineering 2004; 24:

5 886 Nan Wang and Xiaohua Xia / Energy Procedia 61 ( 2014 ) [5] Ge T, Li Y, Wang R, et al. A review of the mathematical models for predicting rotary desiccant wheel. Renewable & Sustainable Energy Reviews 2008; 12: [6] Antonellis SD, Joppolo CM, Molinaroli L. Simulation, performance analysis and optimization of desiccant wheels. Energy and Buildings 2010; 42: [7] Panarasa G, Mathioulakisa E, Belessiotis V, et al. Experimental validation of a simplified approach for a desiccant wheel model. Energy and Buildings 2010; 42: [8] Zhang J, Xia X. A model predictive control approach to the periodic implementation of the solutions of the optimal dynamic resource allocation problem. Automatica 2011; 47: [9] Privara S, Siroky J, Ferkl L, et al. Model predictive control of a building heating system: the first experience. Energy and Buildings 2011; 43: [10]Fanger PO. Thermal comfort: analysis and applications in environmental engineering. New York: McGraw-Hill; Biography Nan Wang is a PhD candidate in the department of Electrical, Electronic and Computer Engineering at the University Of Pretoria, South Africa. His current research interests are human thermal comfort control and HVAC dehumidification systems.