599 research outputs found

    A Review of Static Pressure Reset Control in Variable Air Volume Air Condition System

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    AbstractFor the sake of energy saving of variable air volume (VAV) system, this paper presents the a review of static pressure control around the optimization problem of static pressure reset in VAV air conditioning system. Then, main control methods of static pressure reset are described, and existing problems are analyzed and concluded. Finally, it is pointed out that the critical technology and the development trend of static pressure reset control. This overview is not intended to be an exhaustive survey on this topic, and any omission of other works is purely unintentional

    Computational intelligence techniques for HVAC systems: a review

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    Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

    A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

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    In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

    Screening of energy efficient technologies for industrial buildings' retrofit

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    This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

    Extruder for food product (otak–otak) with heater and roll cutter

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    Food extrusion is a form of extrusion used in food industries. It is a process by which a set of mixed ingredients are forced through an opening in a perforated plate or die with a design specific to the food, and is then cut to a specified size by blades [1]. Summary of the invention principal objects of the present invention are to provide a machine capable of continuously producing food products having an’ extruded filler material of meat or similarity and an extruded outer covering of a moldable food product, such as otak-otak, that completely envelopes the filler material

    Intelligent Approaches For Modeling And Optimizing Hvac Systems

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    Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University. Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building’s HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, reheat (or zone supply air temperature, Tz), and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. The proposed optimization process is validated on an existing VAV system for three summer months (May, June, August). This proposed research deals primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. Recent technological advances in computing power, sensors, and databases will influence the cost savings and scalability of the system. Improved energy efficiencies of existing Variable Air Volume (VAV) HVAC systems can be achieved by optimizing the control sequence leading to advanced BAS programming. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort

    Assessing the Retrofit Potential of Building Control Systems

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    It is well known that building control systems frequently under-perform, leading to wasted energy and poor space conditions for occupants. There are many reasons for this, including insufficient design or commissioning, deterioration of equipment over time, changes in building usage and poor maintenance. Therefore, building control systems are prime candidates for retrofits and upgrades. Such activities, though, can be very challenging in their own right. For example, information regarding the design intent and current control logic may be difficult or impossible to obtain due to lack of documentation, proprietary data and communication formats or unrecorded modifications. In addition, there is a great deal of variability in control system configuration and components, so each potential retrofit activity can become a time-consuming and expensive operation requiring a high level of expertise. To reduce these barriers to implementing building control system retrofits, a method has been developed to assist in the identification and assessment of building control system operation and retrofit potential. The components of the method include a system identification process that categorizes the building by type and usage, then it produces an information model of the control system, which can be compared to other similar buildings by category. Control system requirements to meet two performance levels are provided, namely current best practice and high performance, along with suggested control technology packages to achieve the desired level of performance. This paper describes the method and demonstrates it via a case study
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