Adsorption Desalination and Cooling Systems: Advances in Design, Modeling and Performance

Increasing energy efficiency; reducing energy demand, greenhouse gas emissions, and the use of waste; and integrating renewable and recycled heat from low-temperature sources are significant challenges today and are key parts of 4th Generation District Heating (4GDH) concept. On the other hand, currently about one billion people around the world are suffering from water scarcity, and another three billion are approaching this situation. Only 2.5% of all water on the planet is freshwater, of which around 70% is not available and only 0.4% constitutes the most valuable portion of freshwater. Adsorption cooling technology is one of the most effective ways of addressing both these issues. This technology cools and produces potable water from the renewable and wasted heat of the near ambient temperature, including from sewage water, solar heat, and underground resources. This Special Issue Reprint Book provides the detailed information concerning the above-mentioned issues

Design and Implementation of Fuzzy Controller for Non-Linear Thermally Insulated MIMO Greenhouse Building Utilizing Weather Conditions and Ground Temperature

The increased demand of electricity and water consumption for cooling and heating processes together with the continuous increase in earth temperature due to greenhouse gases emission urged the utilization of sustainable, affordable and clean energy resources. Globally, the biggest amount of water is consumed for agricultural purposes. Domestically, in Abu Dhabi Emirate, the agriculture sector consumes over 50% of the supplied water. Part of this consumption is due to the evaporative cooling approach that is typically used in cooling greenhouses. This approach utilizes a large amount of water and energy to maintain the greenhouse temperature within the desired range. Ground Heat-Exchanger is an environmentally-friendly solution used for heating or cooling applications. It is based on seasonal temperature difference between the ground and the ambient which varies with depth. As depth of ground increases, the temperature fluctuation decreases because of the soil high thermal inertia and the time lag in temperature fluctuation between the surface and the ground. The aim of this thesis is to design a control system using fuzzy logic controller to study the feasibility of utilizing weather conditions and soil temperature in cooling or heating processes of a special type of greenhouses. The proposed control system takes a decision of either utilizing the outside weather conditions or using the soil temperature. The study is conducted on a thermally insulated greenhouse system equipped with ground-to-air heat exchanger, actuated windows, fans, and sensors and the proposed controller performance is compared to a logical and conventional ON/OFF controllers. Results show the proposed control system is capable of maintaining the greenhouse temperature within the desired range for most of the day hours in winter utilizing only the weather and soil temperatures. However, when the temperature is extremely hot, especially in summer, the ground heat exchanger can be only used for pre-cooling with a capability of reducing the ambient temperature of about 6ºC on average. In such extremely hot periods, an auxiliary cooling unit has to be deployed for further cooling. In addition, results reveal that fuzzy controller consumes less power than the logical and the ON/OFF controller when operating the system actuators

Connecting office buildings to the smart grid:harvesting flexibility

Traditionally, the electricity system is oriented top- down and buildings are just energy consumers. Since electricity is expensive to store, supply and demand have to be balanced at all times. In the nearby future, the electricity system must be able to cope with an increase in intermittent decentralized energy production. Also, ongoing electrification is expected to contribute to an increase in demand. Demand side management and control is needed to ensure reliability of supply at acceptable costs. Buildings can be a part of the solution as they can offer flexibility in energy consumption and/or production. By enabling flexible control of processes on the building premises, the building can provide balancing services and respond to congestion problems in the power system, while user comfort can be guaranteed. For the engineering company BAM Techniek, it is of importance to know how the integration of such smart grid technologies in buildings can contribute to (energy) service provision. This study focusses on the enabling of flexibility in energy consumption and generation, while comfort is guaranteed. The project aims to create a framework that enables flexible control of building processes, and analyses of the potential value of flexibility in office buildings. The proposed framework consists of a technical solution, and an analysis of the economical benefits. Priority based control is introduced to enable flexible control of building processes. The concept is capable of prioritizing the energy consumption of processes, and controlling the consumption depending on the needs of the electricity market. An empty office has for instance, a low priority to consume energy. User needs are integrated in the prioritization mechanisms. This mechanism ensures that processes stay within the allowed bandwidth, while providing flexibility to the power system. Since the priority based control connects the end user needs to the market needs, a bi-directional flow of information is required. The Eneco World Office is used to perform a building case study to test the technological framework. Three sources of flexibility are investigated: decentralized climate systems, electric vehicles, and a sensible heat buffer. Results show that the amount of available flexibility depends mainly on load profiles and comfort settings. Electric vehicles and the sensible heat buffer provide significant amounts of flexibility. The flexibility in decentralized climate systems is limited since the room air temperature responds relatively fast to changes in settings and comfort boundaries are quickly met. The long term effect of storage in the building inertia should however be investigated further. Economical benefits can be created by using the variation in costs on the wholesale market caused by market volatility. When flexibility is used to contribute to the balance in a portfolio of buildings, the imbalance can be reduced, which leads to a reduction in costs. Finally, flexibility can contribute to a reduction in peak demand of buildings, leading to cost savings in the network connection. The need for smart grids is growing, while energy services are becoming more important in the built environment. Considering the potential value of smart grid services in the built environment and the market size, it is evident that the developing smart grid market presents opportunities for BAM Techniek. The provision of flexibility services can be a valuable addition to the energy services portfolio

Design of a Robust Priming Controller for SMA Actuators

Shape Memory Alloys (SMAs) have been demonstrated to be effective actuator elements in a wide range of applications, such as robotics, medicine, aerospace and automotive. Enabled by the unique thermo-mechanical properties of SMAs, these actuators offer the advantages of light weight, high power-to-weight ratio and a simple actuation mechanism compared to traditional actuator types. At the same time, the widespread adoption of the SMA actuator remains elusive as its low power efficiency and complex hysteretic behaviour often render it an impractical means of actuation. These actuators also exhibit a slow response speed and their response is highly sensitive to changes in the external environment, namely ambient temperature and mechanical stress, thus complicating their control. Position, force or temperature sensors may be used to facilitate feedback control, but at the cost of increasing the overall size and complexity of the system. The difficulties caused by the hysteretic behaviour can be largely avoided when SMA wires are used as on-off actuators, making SMAs well suited for such applications. However, they may still be subject to a wide range of dynamic operating conditions that would impact their actuation time, and achieving a consistent actuation time is often highly desirable. This thesis presents the synthesis of a nitinol SMA actuator control system which uses electrical resistance feedback to enable a fast response speed and robustness to disturbances in the external environment. A study of the resistance behaviour of SMAs is discussed first. The design of an adaptive controller and the experimental evaluation of its performance are described in detail next. The objective of the SMA actuator control system is to achieve a consistent and fast actuation time throughout the range of operating ambient temperature and stress. The control system is implemented experimentally and shown to be quite successful

Welding Processes

Despite the wide availability of literature on welding processes, a need exists to regularly update the engineering community on advancements in joining techniques of similar and dissimilar materials, in their numerical modeling, as well as in their sensing and control. In response to InTech's request to provide undergraduate and graduate students, welding engineers, and researchers with updates on recent achievements in welding, a group of 34 authors and co-authors from 14 countries representing five continents have joined to co-author this book on welding processes, free of charge to the reader. This book is divided into four sections: Laser Welding; Numerical Modeling of Welding Processes; Sensing of Welding Processes; and General Topics in Welding

Experimental and Numerical Analysis of Ethanol Fueled HCCI Engine

Presently, the research on the homogeneous charge compression ignition (HCCI) engines has gained importance in the field of automotive power applications due to its superior efficiency and low emissions compared to the conventional internal combustion (IC) engines. In principle, the HCCI uses premixed lean homogeneous charge that auto-ignites volumetrically throughout the cylinder. The homogeneous mixture preparation is the main key to achieve high fuel economy and low exhaust emissions from the HCCI engines. In the recent past, different techniques to prepare homogeneous mixture have been explored. The major problem associated with the HCCI is to control the auto-ignition over wide range of engine operating conditions. The control strategies for the HCCI engines were also explored. This dissertation investigates the utilization of ethanol, a potential major contributor to the fuel economy of the future. Port fuel injection (PFI) strategy was used to prepare the homogeneous mixture external to the engine cylinder in a constant speed, single cylinder, four stroke air cooled engine which was operated on HCCI mode. Seven modules of work have been proposed and carried out in this research work to establish the results of using ethanol as a potential fuel in the HCCI engine. Ethanol has a low Cetane number and thus it cannot be auto-ignited easily. Therefore, intake air preheating was used to achieve auto-ignition temperatures. In the first module of work, the ethanol fueled HCCI engine was thermodynamically analysed to determine the operating domain. The minimum intake air temperature requirement to achieve auto-ignition and stable HCCI combustion was found to be 130 °C. Whereas, the knock limit of the engine limited the maximum intake air temperature of 170 °C. Therefore, the intake air temperature range was fixed between 130-170 °C for the ethanol fueled HCCI operation. In the second module of work, experiments were conducted with the variation of intake air temperature from 130-170 °C at a regular interval of 10 °C. It was found that, the increase in the intake air temperature advanced the combustion phase and decreased the exhaust gas temperature. At 170 °C, the maximum combustion efficiency and thermal efficiency were found to be 98.2% and 43% respectively. The NO emission and smoke emissionswere found to be below 11 ppm and 0.1% respectively throughout this study. From these results of high efficiency and low emissions from the HCCI engine, the following were determined using TOPSIS method. They are (i) choosing the best operating condition, and (ii) which input parameter has the greater influence on the HCCI output. In the third module of work, TOPSIS - a multi-criteria decision making technique was used to evaluate the optimum operating conditions. The optimal HCCI operating condition was found at 70% load and 170 °C charge temperature. The analysis of variance (ANOVA) test results revealed that, the charge temperature would be the most significant parameter followed by the engine load. The percentage contribution of charge temperature and load were63.04% and 27.89% respectively. In the fourth module of work, the GRNN algorithm was used to predict the output parameters of the HCCI engine. The network was trained, validated, and tested with the experimental data sets. Initially, the network was trained with the 60% of the experimental data sets. Further, the validation and testing of the network was done with each 20% data sets. The validation results predicted that, the output parameters those lie within 2% error. The results also showed that, the GRNN models would be advantageous for network simplicity and require less sparse data. The developed new tool efficiently predicted the relation between the input and output parameters. In the fifth module of work, the EGR was used to control the HCCI combustion. An optimum of 5% EGR was found to be optimum, further increase in the EGR caused increase in the hydrocarbon (HC) emissions. The maximum brake thermal efficiency of 45% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 10 ppm and 0.61% respectively. In the sixth module of work, a hybrid GRNN-PSO model was developed to optimize the ethanol-fueled HCCI engine based on the output performance and emission parameters. The GRNN network interpretive of the probability estimate such that it can predict the performance and emission parameters of HCCI engine within the range of input parameters. Since GRNN cannot optimize the solution, and hence swarm based adaptive mechanism was hybridized. A new fitness function was developed by considering the six engine output parameters. For the developed fitness function, constrained optimization criteria were implemented in four cases. The optimum HCCI engine operating conditions for the general criteria were found to be 170 °C charge temperature, 72% engine load, and 4% EGR. This model consumed about 60-75 ms for the HCCI engine optimization. In the last module of work, an external fuel vaporizer was used to prepare the ethanol fuel vapour and admitted into the HCCI engine. The maximum brake thermal efficiency of 46% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 5 ppm and 0.45% respectively. Overall, it is concluded that, the HCCI combustion of sole ethanol fuel is possible with the charge heating only. The high load limit of HCCI can be extended with ethanol fuel. High thermal efficiency and low emissions were possible with ethanol fueled HCCI to meet the current demand

PERFORMANCE AND APPLICATIONS OF RESIDENTIAL BUILDING ENERGY GREY-BOX MODELS

The electricity market is in need of a method to accurately predict how much peak load is removable by directly controlling residential thermostats. Utilities have been experimenting with residential demand response programs for the last decade, but inconsistent forecasting is preventing them from becoming a dependent electricity grid management tool. This dissertation documents the use of building energy models to forecast both general residential energy consumption and removable air conditioning loads. In the models, complex buildings are represented as simple grey-box systems where the sensible energy of the entire indoor environment is balanced with the flow of energy through the envelope. When internet-connected thermostat and local weather data are inputs, twelve coefficients representing building parameters are used to non-dimensionalize the heat transfer equations governing this system. The model's performance was tested using 559 thermostats from 83 zip codes nationwide during both heating and cooling seasons. For this set, the average RMS error between the modeled and measured indoor air temperature was 0.44°C and the average daily ON time prediction was 1.9% higher than the data. When combined with smart power meter data from 250 homes in Houston, TX in the summer of 2012 these models outperformed the best traditional methods by 3.4 and 28.2% predicting daily and hourly energy consumption with RMS errors of 86 and 163 MWh. The second model that was developed used only smart meter and local weather data to predict loads. It operated by correlating an effective heat transfer metric to past energy data, and even further improvement forecasting loads were observed. During a demand response trial with Earth Networks and CenterPoint Energy in the summer of 2012, 206 internet-connected thermostats were controlled to reduce peak loads by an average of 1.13 kW. The thermostat building energy models averaged forecasting the load in the 2 hours before, during, and after these demand response tests to within 5.9%. These building energy models were also applied to generate thermostat setpoint schedules that improved the energy efficiency of homes, disaggregate loads for home efficiency scorecards and remote energy audits, and as simulation tools to test schedule changes and hardware upgrades

Monitoring and assessment of weld penetration condition during pulse mode laser welding using air-borne acoustic signal

Real-time monitoring system is one of the essential criteria in the era of the fourth industrial revolution (Industry 4.0). Among the monitoring systems in laser welding applications, acoustic methods have recently caught the attention of researchers due to their benefits in promoting simple, low-cost, and non-contact systems. However, applying this method in PW mode laser was challenging due to the different characteristic of signal and noise acquired from this process as compared to CW process. Therefore, this particular work aims to investigate the characteristics of acoustic sound signal from PW Fiber laser, develop an appropriate signal processing algorithm to suppress the effect of noise on the extracted sound features, and develop an empirical model for weld depth estimation. To achieve the objectives, a 1.8 mm thick 22MnB5 boron steel plate was welded with varied laser peak power (PP) and pulse duration (PD) levels. Simultaneously, the sound signal was acquired between the frequency of 20 Hz to 12.8 kHz throughout the process. Signal features, such as mean absolute deviation (MAD), standard deviation (SD), kurtosis (K), L-scale (LS), L-kurtosis (LK), bandpower (BP), and sum of synchrosqueezed wavelet coefficient (CSqWCsum) were extracted from the acquired sound. To develop the signal processing algorithm, multi-lag phase space (MLPS) method was adopted in which some modifications on its original algorithm were made by introducing the localized crest factor (CF) thresholding method to reduce the influence of noise. Results showed that the acquired sound recorded transient behaviors with a slight change in its overall amplitudes with respect to the change in the level of weld parameters. Meanwhile, the dominant frequency was found to be fluctuated between 5760 Hz and 7000 Hz without a clear pattern in the case of different levels of weld parameters involved in this study. The results from feature selection analysis show that the combination of SD, L-kurtosis, and modified-MLPS recorded the most significant relation with weld penetration. Furthermore, the combination of these features with the laser peak power and pulse duration recorded a better regression trend with an adjusted R-squared of 0.937. Two empirical models for weld depth estimation were developed from the combination of these sound features and weld parameters using the multiple linear regression (MLR) and artificial neural network (ANN) methods. Through MLR method, the obtained model was DOP = 0.634SD - 0.814LK + 0.0014MLPS + 116.44PD + 0.0014PP - 0.7781. Results from the model validation analysis showed that both models could significantly estimate weld penetration during the PW laser welding process with an estimation error less than 8%. However, the ANN model recorded a more accurate and precise estimation with the lowest estimation error, i.e., 3.3%. The results of the analysis suggest that the acoustic methods can be used to monitor weld penetration on a real-time basis during PW mode laser welding process. Moreover, the methods can also be used to provide a quantitative assessment on weld penetration during the process. This finding gives alternative solution to the development of a real-time process monitoring system in PW mode laser welding, which aligns with the criteria needed in the new era of manufacturing system