Extremum Seeking Control for An Air-source Heat Pump Water Heating System with Flash Tank Cycle based Vapor Injection

Vapor injection (VI) techniques have been well received as an effective technology for improving the performance of air-source heat pump (ASHP) under very low ambient temperature, for which the flash tank cycle (FTC) and the internal heat exchanger cycle (IHXC) are the two majaor configurations. In principle, FTC has higher achievable performance than IHXC because that the saturated vapor from the flash tank has a lower temperature which helps reduce the compressor discharge temperature and thus power consumption [1]. However, development of the FTC technology has been hampered by the lack of proper control/operational strategy that can optimize the thermodynamic characteristics of the vapor injection channel under variable ambient and load conditions. In the flash tank, the refrigerant is separated into the liquid and saturated vapor phase. The liquid refrigerant enters the lower-stage expansion valve and then circulates through the evaporator before entering the suction side of compressor, while the saturated-vapor refrigerant is injected into the intermediate pressure port of compressor. As saturated vapor is in principle the best choice for the vapor injection channel, superheat adjustment via the upper electronic expansion valve (EEV) is no longer viable. A liquid level measurement for the flash tank has been considered as feedback for the EEV control, however, determination of the optimum liquid level is rather difficult for practical operation due to the complexity of the underlying process and diversity in operating condition. In this paper, we propose an extremum seeking control (ESC) based strategy for efficient operation of the FTC-VI based ASHP heating systems [3]. ESC is a model-free real-optimization strategy, which is a dynamic gradient search with the online gradient estimation realized by a dither-demodulation scheme. For this problem, the setpoint for the intermediate pressure of injected saturated vapor is adopted as the manipulated input of the ESC, which is adjusted by the opening of the upper EEV via an inner-loop proportional-integral (PI) controller; while the total power consumption of the system is the only feedback needed. The heating load is regulated by the compressor capacity. To evaluate the proposed ESC strategy, a Modelica based dynamic simulation model of an FTC-VI based ASHP water heater is developed with Dymola and TIL Library. The hot-water outlet temperature is regulated by the compressor capacity, while the upper-EEV opening is used to regulate the intermediate pressure and liquid level of the flash tank. Simulation study is performed under different scenarios of ambient and thermal load conditions. The results show that the ESC is able to find the optimum intermediate pressure (corresponding to the optimum flash tank liquid level) by adjusting the upper EEV, which minimizes the total power consumption without sacrifice of heating load regulation and thus maximizes the system COP. To the authors’ best knowledge, the proposed strategy is a novel control solution to the optimal operation of FTC-VI ASHP systems, which does not require plant models or sensor measurements beyond power consumption. The presented results promises a great potential for the proposed strategy to facilitate the adoption of FTC technology

Modelling tumbling ball milling based on DEM simulation and machine learning

Tumbling ball milling is a critical comminution process in materials and mineral processing industries. It is an energy intensive process with low energy efficiency. It is important that ball mills and the milling process are properly designed and operated. To achieve this, models at different scales are needed to provide accurate prediction of mill performance under various conditions. This study aimed to develop a combined discrete element method (DEM) and machine learning (ML) modelling framework to link mill design, operation parameters with particle flow and mill efficiency. A scale-up model was developed based on DEM simulations to link mill size ratio, rotation rate, and filling level with power draw and grinding rate. Then, an ML model using the Support Vector Machine (SVM) algorithm was developed to predict the angle of repose (AoR) and collision energy based on various operation conditions. The ML model was trained by the data generated from the DEM simulations and able to predict the AoR and collision energy. In the process monitoring, an artificial neural network (ANN) was firstly proposed to predict internal particle flow properties of a rotating mill based on acoustic emission (AE) signal generated using the DEM. Main features of AE signals and power draw were fed into the ANN to predict flow properties such as particle size distributions, collision energy distribution and filling levels. Further, a convolution neural network (CNN) was used to replace the ANN to extract more efficient features of AE signals non-linearly based on different local frequency ranges in a ball milling process partially filled with steel balls and grinding particles. Last, a physics-informed ML model was developed based on continuous convolution neural network (CCNN) to learn particle contact mechanisms provided by DEM data at different rotation speeds. The ML model coupled with DEM simulation can accelerate DEM simulation to accurately predict particle flow in a long time series. In summary, this work has demonstrated that combining physics-based numerical models DEM to ML models not only improves the efficiency and accuracy of predictions of complicated processes but also provides more insight to the process and makes predictions more transparent

Self-optimizing Control of Cooling Tower for Efficient Operation of Chilled Water Systems

The chilled-water systems, mainly consisting of electric chillers and cooling towers, are crucial for the ventilating and air conditioning systems in commercial buildings. Energy efficient operation of such systems is thus important for the energy saving of commercial buildings. This paper presents an extremum seeking control (ESC) scheme for energy efficient operation of the chilled-water system, and presents a Modelica based dynamic simulation model for demonstrating the effectiveness of the proposed control strategy. The simulated plant consists of a water-cooled screw chiller and a mechanical-draft counter-flow wet cooling tower. The ESC scheme takes the total power consumption of the chiller compressor and the tower fan as feedback, and uses the fan speed setting as the control input. The inner-loop controllers for the chiller operation include two proportional-integral (PI) control loops for regulating the evaporator superheat and the chilled water temperature. Simulation was conducted on the dynamic simulation model of the whole plant including the screw chiller and the cooling tower for different scenarios. The simulation results demonstrated the effectiveness of the proposed ESC strategy in searching for the optimal tower fan speed set-point under tested circumstances, and the potential for energy saving is also evaluated

Multi-variable Extremum Seeking Control for Mini-split Air-conditioning System

In this study, a multi-variable extremum seeking control (ESC) scheme is proposed for a variable-speed mini-split air-conditioning system. The control inputs are the evaporator and condenser fan speeds, respectively, while the total power consumption is used as the feedback. As accurate model is hard and expensive to obtain for the AC system of interest in real time, nearly model-free self-optimizing control methods such as ESC is considered a more feasible solution to practical deployment. Recent development in ESC, and especially the Newton based multi-variable ESC method with online Hessian estimation provides the capability for real-time decoupling among the input channels (Ghaffari et al. 2012). Different from gradient based multi-variable ESC method, the Newton based multi-variable ESC provides uniform convergence characteristics for all the control inputs. Therefore, the Newton based multi-variable ESC is suitable for multi-input real-time optimization, especially for the case with large gain variation and coupling for different control input channels. An experimental setup is developed with a 9000 BTU variable-speed mini-split AC system (Mitsubishi MSZ-GE09NA & MUY-GE09NA). A 2000 Watts electrical heater works as the heat load. The indoor unit of the mini-split system and the heater are installed in a 4’x8’x6’ insulated chamber. A Watt Node Pulse WNB-3D-240-P power meter is utilized to measure the power consumption of the mini-split system. To achieve the speed control of the evaporator fan motor and condenser fan, a TMS320F28035 based customized motor controller is used. Three RTD temperature sensors are deployed to measure the indoor temperature, the outdoor temperature and the condenser coil temperature, respectively. The data acquisition and control algorithms are implemented on a National Instruments CompactRIO platform. During the system operation, the CompactRIO reads the power consumption sent from the power meter, which will be fed into the ESC control algorithm to get the speed reference for both the evaporator fan and the condenser fan. Then, the speed reference will be applied to the motor controllers for each motor. Meanwhile, some other measurements such as indoor temperature, outdoor temperature, the speed feedback for both the motors, etc. are also monitored by the CompactRIO. The experimental study is planned to include three scenarios of ESC implementation: 1) single-input ESC with evaporator fan speed input only; 2) single-input ESC with condenser fan speed input only; and 3) multi-variable ESC with both evaporator and condenser fan speed inputs. Experimental study has performed for the first scenario. Under the ambient temperature of 75F and indoor room temperature set-point of 68F, the ESC control results in an energy saving of 20%. The work under way includes the other two scenarios and in particular the multi-variable ESC. More experiments will be performed under various weather conditions

Experimental Study on Extremum Seeking Control for Efficient Operation of Air-side Economizer

The air-side economizers are a major class of energy-saving devices for ventilation and air conditioning systems by taking advantage of outdoor air during cool or cold weather. Typical rule based control cannot justify energy optimal operation, while model based optimization of air-side economizer operation depends on the accurate knowledge of system model and enthalpy sensing of the ambient and return-air. Such optimal operation is hard to achieve in practice due to inaccurate model and degradation/failure of temperature and relative humidity (RH) sensors. As pointed out by Seem and House (2010), under certain indoor/outdoor air conditions, there exists a convex map between damper position and energy consumption of an air handling unit (AHU), which implies an optimal damper opening minimizing the cooling-coil load. Such convexity guarantees the use of gradient-search type of real-time optimization methods. An Extremum Seeking Control (ESC) was proposed by Li et al. (2009), where the chilled water flow rate of the cooling coil (equivalently the energy consumption) is minimized by tuning the damper opening. The proposed framework was validated with a Modelica based dynamic simulation model of an air-side economizer. This study is conducted to perform experimental evaluation of the ESC control of air-side economizer. The experimental setup is anchored on a Lennox XC25 variable-speed air conditioner. The Lennox, CBX40UHV indoor air handler unit is equipped with duct work to form an air-side economizer, connected to a foam based 16\u27X8\u27X8\u27 test chamber. The Lasko 751320 electrical heaters are used as heat source. The Honeywell HCM-890 humidifiers and Soleus Air SG-DEH-70EIP-6 dehumidifiers are used to regulate the indoor air humidity. A National Instruments CompactRIO-9024 platform is used for data acquisition and control. Major measurements include temperature, relative humidity (RH) and power consumption. A Watt Node Pulse WNB-3D-240-P electric power meter is used for power measurement. The Omega P-L-1/10-1/8-6-0-T-3 temperature sensors and Veris Industries HN3XVSX RH sensors are installed to monitor the indoor and outdoor air conditions. The Omega HHT13 speed sensors are used to measure fan speeds, while Fluke 80i-110s current sensors are used to measure the compressor motor current. The ESC controller is implemented with the damper opening as input and the total power consumption as feedback. Two experiments have been performed under different indoor/outdoor air conditions. The first experiment was performed under outdoor air temperature 23°C and RH 65%, a heat load of 6000 W and indoor temperature setpoint 28°C. The ESC turned on the outdoor damper 100% automatically to allow maximal outdoor air resulting in indoor RH 50%. The total power consumption was reduced from 540 W to 450 W with an energy saving of 16.67%. The second experiment was performed under same conditions with indoor RH regulated to 40%.The ESC turned off the outdoor damper to allow minimal outdoor air. The power consumption was reduced from 620 W to 600 W with an energy saving of 3.33%. More experiments will be performed in warmer weather in February and March to further validate the performance of the ESC controller

Model-free Control and Automatic Staging of Variable Refrigerant Flow System with Multiple Outdoor Units

For efficient operation of a variable refrigerant flow (VRF) air conditioning system with multiple outdoor units (ODUs), we propose a model-free control strategy based on extremum seeking control along with automatic staging control logic. The proposed strategy is evaluated with a representative VRF system consisting of 12 indoor units (IDUs) and three ODUs. The IDU zone temperature is regulated by EEV opening, and the compressor pressure is regulated by compressor speed. To optimize load sharing among multiple ODUs in operation, a set of bypass valves (BPVs) are added to the suction side of the compressors to manipulate refrigerant flow distribution among different compressors as needed. A penalty-function based multivariable extremum seeking control (ESC) method is used for real-time optimization of system operation. The performance index as the ESC feedback is the total power of the compressors, the ODU fans and the IDU fans, augmented with penalties for securing minimum superheat at the suction side of compressors. The manipulated inputs include the compressor suction pressure setpoint, the openings of BPVs at the suction side of the compressors, and a uniform setpoint of fan speed for all ODUs. As for the ESC feedback, the compressor power is normalized by its capacity. A set of control strategies for staging on/off particular ODUs is developed based on the compressor speed of the operating ODUs. Under increasing load, if the operating compressor(s) speed exceeds the higher limit of operation speed range (80% of rated speed), an additional ODU turned on to meet the load demand. Under decreasing load, it is desirable to turn off the least efficient ODU in a model-free fashion. In this study, an ESC based ODU staging-off strategy is proposed, for which the compressor shaft power normalized by the rated capacity is adopted as the ESC input. In addition to the compressor pressure setpoints and ODU fan speeds, the manipulated inputs of ESC also include the openings of suction-side BPVs in order to optimize load sharing among the multiple ODUs. With online optimization of ODU load sharing based on the normalized compressor power, the ESC can drive less efficient compressor(s) to operate at lower speed/capacity. If the compressor speed of an ODU falls below the preset lower limit of operational speed range (e.g. 20% of the rated speed) for long enough time, this ODU will be turned off. A dynamic simulation model of the multi-ODU VRF system is developed with Dymola and TIL Library. Simulation studies have been performed to evaluate the proposed ESC strategy for energy efficient operation during constant load patterns and the control logic for staging on and off ODU during load increase and decrease. The total power searched by the ESC is shown to be close to that obtained by a genetic algorithm based global optimization procedure in Dymola. Also, ESC is shown to be able to turn off least efficient ODU during load decrease without model knowledge. The load-sharing BPV at the compressor suction-side demonstrates bearable pressure loss except for the scenarios of large split ratio