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With the increase in electricity consumption and price, energy efficiency in industrial refrigeration systems becomes an even more important issue. The performance of the industrial refrigeration system can be reported by creating energy labeling according to the energy efficiency index value. In this study, the industrial refrigeration system was evaluated in terms of energy performance with proposed a fan control technique and performed an environmentally friendly design using propane (R290) refrigerant. In addition, a new control process was developed for the management of evaporator and condenser fans, and the effect of this process on the cooled package temperatures and energy consumption was investigated. With this new technique, a 25.2% improvement was achieved in the total energy consumption of the industrial refrigeration system. It was observed that the cooled package temperatures were between −1 and +5°C throughout the experiment. The EEI value of the industrial refrigeration system has increased from class "E" to class "D" using a new control technique. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) aims to expand the benefits of being connected to the Internet network continuously. It functions as a control system that has been widely applied in various fields because in certain case people are not allowed in certain rooms for security reasons. The research aimed to create a temperature and humidity monitoring system using as many fan controls as expected by utilizing IoT. The model used input in the form of temperature and humidity sensors. The output was a motor driver that drove a fan and used a microcontroller as the main processor. IoT-based systems consisted of hardware and software. Hardware included NodeMCU ESP8266 V3, DHT22 sensor, L298N motor driver module, fan, and computer. Meanwhile, the microcontroller software was made using Arduino IDE. From the test results, the system model works well. Fan control is set manually based on desired room temperature and humidity monitoring based on IoT. A mobile phone can also monitor temperature and humidity and control fans. The DHT22 sensor can read temperature and humidity every two seconds so that the resulting data is stable to display. Then, the L298N motor driver can adjust the fan speed with Pulse Width Modulation (PWM) using analog data ranging from 1 to 1.024.

The present paper aims at using an artificial intelligence algorithm to minimize the fan power consumption in air-cooled servers. The proposed algorithm can handle the complex thermal environments within the servers to tailor the influences and interactions amid numerous heat sources, airflow, bypass phenomenon, fan operation, and the transient operations. Modified correlations are first proposed to effectively predict the thermal-hydraulic performance of heat sinks and the corresponding predictive ability against Nusselt number and pressure drop is within 5.0% and 10%, respectively. Without the algorithm control, the maximum deviation between the prediction and the experimental data is within 2.0 °C. By introducing the deep reinforcement learning (DRL) algorithm subject to the interactions of complex thermal environments, the fan power consumption can be saved by 55.7%, 40.3% and 26.3%, respectively, in comparison with the strategy with 100% fan duty. Yet the DRL agent still offers 16.7% energy saving when compared to a fixed 40% fan duty.

In this work, we present a control system for a mini electrical fan. The aim of this work is to design a controller prototype for an electrical fan within the smart home system. The system consists of an STM32L100 microcontroller for controlling the electrical fan using pulse-width modulation (PWM), TIP 102 BJT transistor for 'off,' and 'on' control of the fan, and 1N4007 fly-back diode. The PWM is generated by the STM32L100 microcontroller to control the Fan's speed softly (25%, 50%, 75%, and 0% - 100% of speed). In addition, the system is equipped with a ZigBee module to support wireless communication with the host, which is required to receive and process commands from the Android application used for the user interface. Moreover, with the Zigbee module, the fan device enables be integrated wirelessly with other end devices in a smart home environment through a network (e.g., Mesh topology). Based on the testing performed, the system can work as intended, it can be controlled easily using Android smartphone and its speed, with the currents of 43.1 mA (idle mode) and 145.1 mA (processing mode) powered by 12 VDC. [ABSTRACT FROM AUTHOR]

In developing countries small-scale dryers and storage facilities are mostly monitored and controlled manually, which is complicated and prone to errors. Simple, affordable control devices can help farmers in the management of their systems. In this study, a low-cost, low-energy microcontroller-based humidistat was constructed to control the operation of a photovoltaic driven DC fan. The fan was connected to a modification of a typical Ethiopian drying and storage structure for ear maize. Fan current and voltage, insolation, and ambient temperature and relative humidity were recorded at five minutes intervals to test whether the humidistat performed as expected. The humidistat is reliable, costed less than 10 €, and can be easily modified in software and/or hardware to control fans or other components based on other criteria. [ABSTRACT FROM AUTHOR]

On-farm, in-bin drying of corn is weather dependent process that requires a careful selection of drying strategy in a locality to prevent reduction in the grain quality. This study simulated drying and storage of corn using different fan control methods and assessed the impact of infrared heat treatments to decontaminate corn of harmful molds spores. Fan operation considered included running the fan continuously, only at night (NANO), only during the day (NADO), at a set window of equilibrium moisture content of natural air, and when supplemented with heating of ambient air. The drying was investigated at various initial moisture contents (16 to 22%, wet basis), air flow rates (1.11 to 4.46 m3 min−1[air] t−1 [corn]) and corn harvesting start dates (August 15-November 15). To prevent aflatoxin contamination, freshly-harvested corn should be dried within 10 days to MC below 15% if prevailing relative humidity exceeds 86%. [ABSTRACT FROM AUTHOR]

On-farm, in-bin drying and storage of soybean in environments with unconditioned air often result in repeated drying and rewetting of the grains which may have adverse effects on quality metrics; if done using natural air, as recommended for soybean destined to the seed market, the in-bin drying and storage method require operation at well-defined local weather-dependent strategies to maintain the seed quality. This study simulated in-bin drying and storage of soybeans. Different fan control options and drying strategies were used to assess performance in terms of drying duration to target final moisture content (MC), percent over drying, energy expenditure, and drying cost. Fan operation included running the fan continuously, only at night, only during the day, at a set window of equilibrium MC (EMC) of natural air, and set EMC window with supplemental heating of ambient air as an option (EMC-H). Drying and storage performance were tested for soybean at initial moisture content (IMC) (16–22%, wet basis), air flow rate (1.04–5.0 m3 min−1 [air] t−1 [soybean]), and harvesting start dates (August 15 to November 15). Simulation model was validated using a bench-scale pressure drop system filled with soybeans with IMC of 22% wet basis. The result shows that fan control strategies, air flow rates, harvest date, and initial MC of the soybeans significantly (P < 0.05) impact the drying duration, percent over drying, and final MC of the grain. The simulation predicted well the overall profile of the experimental data. Layer by layer statistical comparison revealed that the overall mean relative deviation was less than 10%, low values of root mean square error (ranged between 2.4 and 2.5), Mallows' criteria (ranged between 2.4 and 7.0), and overall χ2 was 0.88. [ABSTRACT FROM AUTHOR]

En este trabajo se implementó el control de un ventilador industrial utilizado para prueba de aerogeneradores, el cual es empleado como un dispositivo interno en la elaboración de un túnel de viento, para realizar pruebas de medición de viento. El proyecto consistió en regular la velocidad de un ventilador industrial utilizado en un túnel de viento, para realizar pruebas de sistemas de control en aerogeneradores, generándose señales de viento reguladas en valores constantes, ó que sigan un perfil definido por una base de datos de valores reales medidos con un anemómetro. Para implementar el sistema de control y la comunicación de los dispositivos, se empleó un procesador digital de señales (PDS de Texas Instruments EZDSP2407), que actúa como interfaz para transmitir los datos entre el entorno de programación (VisSim Embedded Control Developer (ECD)). Además se utilizó un variador de velocidad de 3HP de la marca SIEMENS modelo Micromaster 420. In this work, a fan control industrial wind turbines used for test, which is used as an internal device in the development of a wind tunnel for testing wind measurement. The project consists of regulating the speed of an industrial fan used in a wind tunnel to test control systems in wind turbines, wind generating regulated signals in constant, or to follow a profile defined by a database of values actual measured with an anemometer. To implement the control system and communication devices, in this project employed a digital signal processor (DSP from Texas Instruments EZDSP2407), which acts as an interface to transmit data between the programming environments (VisSim Embedded Control Developer (ECD)). Also uses a variable speed 3HP SIEMENS Micromaster model 420.

In this work, a fan control industrial wind turbines used for test, which is used as an internal device in the development of a wind tunnel for testing wind measurement. The project consists of regulating the speed of an industrial fan used in a wind tunnel to test control systems in wind turbines, wind generating regulated signals in constant, or to follow a profile defined by a database of values actual measured with an anemometer. To implement the control system and communication devices, in this project employed a digital signal processor (DSP from Texas Instruments EZDSP2407), which acts as an interface to transmit data between the programming environments (VisSim Embedded Control Developer (ECD)). Also uses a variable speed 3HP SIEMENS Micromaster model 420. [ABSTRACT FROM AUTHOR]

In this work, a fan control industrial wind turbines used for test, which is used as an internal device in the development of a wind tunnel for testing wind measurement. The project consists of regulating the speed of an industrial fan used in a wind tunnel to test control systems in wind turbines, wind generating regulated signals in constant, or to follow a profile defined by a database of values actual measured with an anemometer. To implement the control system and communication devices, in this project employed a digital signal processor (DSP from Texas Instruments EZDSP2407), which acts as an interface to transmit data between the programming environments (VisSim Embedded Control Developer (ECD)). Also uses a variable speed 3HP SIEMENS Micromaster model 420. [ABSTRACT FROM AUTHOR]

Fans are one of the components performing cooling whose power consumption can be reduced by precise, energy-aware control. Such energy-efficient fan controllers must carefully manage fan speeds in order to remain as close as possible to the optimal setting, simultaneously minimizing the power leakage due to heat and fan power consumption, while avoiding overheating. The following paper presents a new Dynamic Fan Controller for Energy (DFaCE) for minimizing the energy consumption of several fans. DFaCE learns the optimal fan setting with no prior knowledge of the hardware setup nor the governing physics, all the while performing useful work on the computer. Once the optimal fan setting is found, the system immediately applies it to minimize the power consumption of the computer at no cost. The system is also evaluated over a set of benchmarks and achieves up to 46 % in the cooling subsystem compared to common temperature-driven control strategies. [ABSTRACT FROM AUTHOR]

Grains (common term referring to cereal grains, oilseeds, pulses) are usually harvested at high moisture content and then dried to straight grade (dry) or safe storage moisture levels. Grain drying in freestanding, corrugated galvanized steel or welded steel bins using natural air is the most cost effective drying method with optimum grain quality. Adverse weather conditions and inappropriate fan control strategies may result in poor drying, higher drying cost (electricity and fuel), and grain spoilage. Several traditional fan control (continuous ON, only Day ON, only Night ON) and automated fan control (Natural Air Drying (NAD) and Self- Adapting Variable Heat (SAVH)) strategies were investigated using IntegrisPro model software (OPIsystems Inc.) for their drying potential for wheat using 30 years of historical weather data (1983-2012) from 14 locations in Western Canada (Alberta, Manitoba, and Saskatchewan provinces) which cover nearly 75% of Canada's farming area. Effects of initial wheat moistures (20, 18, and 16%), start dates (August 20th, September 1st, September 15th, and October 1st), locations, airflow rates (0.52, 0.78, and, 1.04 m3min-1t-1) and supplemental heat on drying performance were studied. High moisture wheat (18-20%) can only be dried effectively with sufficient airflow rate (0.78, and, 1.04 m3min-1t-1) and early drying start date (September 15th or earlier). Without automated control, Continuous ON fan control was a better control strategy. The SAVH control strategy gave the optimized results in terms of fan run hours, target moisture, moisture spread, and shrink (over-drying). The Night ON fan control strategy gave the poorest drying results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multitier approach for significantly lowering the cooling costs associated with fan subsystems without compromising the system performance. Our technique manages the fan speed by intelligently allocating the workload at the core level as well as at the CPU socket level. At the core level we propose a proactive dynamic thermal management scheme. We introduce a new predictor that utilizes the band-limited property of the temperature frequency spectrum. A big advantage of our predictor is that it does not require the costly training phase and still maintains high accuracy. At the socket level, we use control theoretic approach to develop a stable scheduler that reduces the cooling costs further by providing a better thermal distribution. Our thermal management scheme incorporates runtime workload characterization to perform efficient thermally aware scheduling. The experimental results show that our approach delivers an average cooling energy savings of 80% compared to the state of the art techniques. The reported results also show that our formal technique maintains stability while heuristic solutions fail in this aspect. [ABSTRACT FROM PUBLISHER]

Abstract: Fish products are frozen to preserve quality and extend shelf-life. However, freezing processes in the industry are typically very energy demanding and seldom optimized with regard to energy usage. During freezing, the operating conditions for the refrigeration cycle, as well as the driving temperature difference over the product changes significantly from start to finish. A complete transient model including a refrigeration plant, an air blast freezing tunnel and food products has been built, based on the Modelica programming language. The product model is discretized into uniform layers, described with equations for temperature dependent properties such as thermal conductivity and heat capacity. Normally, fan power represents about 25 – 30% of the total refrigeration requirement, but at the end of the freezing process, heat from the fans can represent up to 95-99% of the refrigeration load. The results from this model indicates that a 33% reduction in total power consumption, with a penalty of 14% longer freezing time is possible with better operation of the fan. In general, this model can be a useful tool for visualization of energy saving measures. It combines a product model with a refrigeration system, demonstrating the effect of process modification on both single components and overall process performance. [Copyright &y& Elsevier]