Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results.

The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control.

An Innovative Ultrasonic Apparatus and Technology for Diagnosis of Freeze-Drying Process.

The freeze-drying process removes water from a product through freezing, sublimation and desorption procedures. However, the extreme conditions of the freeze-drying environment, such as the limited space, vacuum and freezing temperatures of as much as -50 °C, may block the ability to use certain diagnostic sensors. In this paper, an ultrasonic transducer (UT) is integrated onto the bottom of a specially designed frozen bottle for the purpose of observing the freeze-drying process of water at varying amounts. The temperatures and visual observations made with a camera are then compared with the corresponding ultrasonic signatures. Among all of the diagnostic tools and technologies available, only ultrasonic and visual records are able to analyze the entire progression of the freeze-drying process of water. Compared with typical experiment settings, the indication of drying point for water by the amplitude variations of ultrasonic L3 echo could reduce the process period and energy consumption. This study demonstrates how an innovative frozen bottle, an integrated ultrasonic sensor and diagnostic methods used to measure and optimize the freeze-drying process of water can save energy.

Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis.

In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

Fabrication and characterization of distinctive ZnO/ZnS core-shell structures on silicon substrates via a hydrothermal method.

A distinctive novel ZnO/ZnS core-shell structure on silicon was reported in this study. Compared with previous studies, ZnO nanorods encapsulated by 5 nm ZnS nanograins were observed using a scanning electron microscope. Furthermore, strong (111) cubic ZnS crystalline structures were confirmed using high resolution transmission electron microscopy, selected area diffraction, and X-ray diffraction. The optical properties changed and the antibacterial behaviors were suppressed as the ZnS shells were attached onto the ZnO nanorods. Moreover, the results also indicate that the hydrophobicity could be enhanced as more ZnS nanograins were wrapped onto the ZnO nanorods. The ZnO/ZnS core-shell structures in this research show promise for use in future optoelectronic and biomedical applications.

Enabling Smart Air Conditioning by Sensor Development: A Review.

The study investigates the development of sensors, in particular the use of thermo-fluidic sensors and occupancy detectors, to achieve smart operation of air conditioning systems. Smart operation refers to the operation of air conditioners by the reinforcement of interaction to achieve both thermal comfort and energy efficiency. Sensors related to thermal comfort include those of temperature, humidity, and pressure and wind velocity anemometers. Improvements in their performance in the past years have been studied by a literature survey. Traditional occupancy detection using passive infra-red (PIR) sensors and novel methodologies using smartphones and wearable sensors are both discussed. Referring to the case studies summarized in this study, air conditioning energy savings are evaluated quantitatively. Results show that energy savings of air conditioners before 2000 was 11%, and 30% after 2000 by the integration of thermo-fluidic sensors and occupancy detectors. By utilizing wearable sensing to detect the human motions, metabolic rates and related information, the energy savings can reach up to 46.3% and keep the minimum change of predicted mean vote (∆PMV→0), which means there is no compromise in thermal comfort. This enables smart air conditioning to compensate for the large variations from person to person in terms of physiological and psychological satisfaction, and find an optimal temperature for everyone in a given space. However, this tendency should be evidenced by more experimental results in the future.

Novel real-time diagnosis of the freezing process using an ultrasonic transducer.

The freezing stage governs several critical parameters of the freeze drying process and the quality of the resulting lyophilized products. This paper presents an integrated ultrasonic transducer (UT) in a stainless steel bottle and its application to real-time diagnostics of the water freezing process. The sensor was directly deposited onto the stainless steel bottle using a sol-gel spray technique. It could operate at temperature range from -100 to 400 °C and uses an ultrasonic pulse-echo technique. The progression of the freezing process, including water-in, freezing point and final phase change of water, were all clearly observed using ultrasound. The ultrasonic signals could indicate the three stages of the freezing process and evaluate the cooling and freezing periods under various processing conditions. The temperature was also adopted for evaluating the cooling and freezing periods. These periods increased with water volume and decreased with shelf temperature (i.e., speed of freezing). This study demonstrates the effectiveness of the ultrasonic sensor and technology for diagnosing and optimizing the process of water freezing to save energy.

Novel real-time temperature diagnosis of conventional hot-embossing process using an ultrasonic transducer.

This paper presents an integrated high temperature ultrasonic transducer (HTUT) on a sensor insert and its application for real-time diagnostics of the conventional hot embossing process to fabricate V-cut patterns. The sensor was directly deposited onto the sensor insert of the hot embossing mold by using a sol-gel spray technique. It could operate at temperatures higher than 400 °C and uses an ultrasonic pulse-echo technique. The ultrasonic velocity could indicate the three statuses of the hot embossing process and also evaluate the replication of V-cut patterns on a plastic plate under various processing conditions. The progression of the process, including mold closure, plastic plate softening, cooling and plate detachment inside the mold, was clearly observed using ultrasound. For an ultrasonic velocity range from 2197.4 to 2435.9 m/s, the height of the V-cut pattern decreased from 23.0 to 3.2 µm linearly, with a ratio of -0.078 µm/(m/s). The incompleteness of the replication of the V-cut patterns could be indirectly observed by the ultrasonic signals. This study demonstrates the effectiveness of the ultrasonic sensors and technology for diagnosing the replicating condition of microstructures during the conventional hot embossing process.

Smart sensors enable smart air conditioning control.

In this study, mobile phones, wearable devices, temperature and human motion detectors are integrated as smart sensors for enabling smart air conditioning control. Smart sensors obtain feedback, especially occupants' information, from mobile phones and wearable devices placed on human body. The information can be used to adjust air conditioners in advance according to humans' intentions, in so-called intention causing control. Experimental results show that the indoor temperature can be controlled accurately with errors of less than ±0.1 °C. Rapid cool down can be achieved within 2 min to the optimized indoor capacity after occupants enter a room. It's also noted that within two-hour operation the total compressor output of the smart air conditioner is 48.4% less than that of the one using On-Off control. The smart air conditioner with wearable devices could detect the human temperature and activity during sleep to determine the sleeping state and adjusting the sleeping function flexibly. The sleeping function optimized by the smart air conditioner with wearable devices could reduce the energy consumption up to 46.9% and keep the human health. The presented smart air conditioner could provide a comfortable environment and achieve the goals of energy conservation and environmental protection.