2,869 research outputs found
Extruder for food product (otak–otak) with heater and roll cutter
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
An improved artificial dendrite cell algorithm for abnormal signal detection
In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy
Fuzzy Logic Based Controller for Maintaining Human Comfort within Intelligent Building System
This paper presents an intelligent control approach for air handling unit (AHU)
which is an integral part of heat, ventilation, and air conditioning (HVAC) system. In the
past years various control design for HVAC have been proposed as this system
remarkably consumes very high energy. But most of the proposed designs were focused
on the control flow of heat-transfer medium such as chilled or heated water while the
importance of the efficient mixture of outdoor and indoor enthalpies is sometimes
ignored. These enthalpies invariably determine the best strategy to overcome thermal
load in a controlled environment to satisfy human comfort, hence a control design
strategy must be able to efficiently regulate the flow and mixture of outdoor and indoor
enthalpies by a proper control of AHU dampers and fans. This approach requires sensors
to measure temperature and relative humidity of both outdoor and indoor environments.
However, unpredictable level of disturbances coming from many sources including heat
generated by occupants, electrical items and air leaking and the continuous changes of
outdoor enthalpy makes it difficult to model the process. Consequently, conventional
controllers are not suitable, hence the use of fuzzy logic controller (FLC) is proposed in
this paper. This proposed controller operates in a master and slave control loop so as to
control the AHU dampers and fans with adjustable output membership function whilst at
the same time a scaling-factor method is used to drive the master operation. To
implement the proposed system, a small scale prototype has been designed and
fabricated. This prototype is an AHU model which consists of ductwork, temperature
and humidity sensors, dampers, air cooling and heating systems. A small box is used as a
conditioning space in which a room temperature is measured. The control algorithm is
programmed using National Instrument (NI) LabVIEW and executed using NI
FieldPoint. Experimental results reveal that proper control of AHU dampers and fans is
an effective and practical means to satisfy human comfort with minimum energy
consumption
Functions of fuzzy logic based controllers used in smart building
The main aim of this study is to support design and development processes of advanced fuzzy-logic-based controller for smart buildings e.g., heating, ventilation and air conditioning, heating, ventilation and air conditioning (HVAC) and indoor lighting control systems. Moreover, the proposed methodology can be used to assess systems energy and environmental performances, also compare energy usages of fuzzy control systems with the performances of conventional on/off and proportional integral derivative controller (PID). The main objective and purpose of using fuzzy-logic-based model and control is to precisely control indoor thermal comfort e.g., temperature, humidity, air quality, air velocity, thermal comfort, and energy balance. Moreover, this article present and highlight mathematical models of indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm
Computational intelligence techniques for HVAC systems: a review
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
An investigation into the energy and control implications of adaptive comfort in a modern office building
PhD ThesisAn investigation into the potentials of adaptive comfort in an office
building is carried out using fine grained primary data and computer
modelling. A comprehensive literature review and background study into
energy and comfort aspects of building management provides the
backdrop against which a target building is subjected to energy and
comfort audit, virtual simulation and impact assessment of adaptive
comfort standard (BS EN 15251: 2007). Building fabric design is also
brought into focus by examining 2006 and 2010 Approved Document
part L potentials against Passive House design. This is to reflect the
general direction of regulatory development which tends toward zero
carbon design by the end of this decade. In finishing a study of modern
controls in buildings is carried out to assess the strongest contenders that
next generation heating, ventilation and air-conditioning technologies
will come to rely on in future buildings.
An actual target building constitutes the vehicle for the work described
above. A virtual model of this building was calibrated against an
extensive set of actual data using version control method. The results
were improved to surpass ASHRAE Guide 14. A set of different scenarios
were constructed to account for improved fabric design as well as
historical weather files and future weather predictions. These scenarios
enabled a comparative study to investigate the effect of BS EN
15251:2007 when compared to conventional space controls.
The main finding is that modern commercial buildings built to the latest
UK statutory regulations can achieve considerable carbon savings
through adaptive comfort standard. However these savings are only
modestly improved if fabric design is enhanced to passive house levels.
Adaptive comfort can also be readily deployed using current web-enabled
control applications. However an actual field study is necessary to
provide invaluable insight into occupants’ acceptance of this standard
since winter-time space temperature results derived from BS EN
15251:2007 constitute a notable departure from CIBSE environmental
guidelines
Self-Adaptive Model-Based Control for VAV Systems
In North America one of the main users of primary energy are buildings, where HVAC equipment operation is the largest consumer of total energy by end use. This has triggered the need to develop better active strategies and building technologies for the enhancement of HVAC equipment performance. Great examples of solutions that large commercial and institutional buildings adopted, were the widespread use of Building Automation Systems (BAS), and approaches like Variable Air Volume (VAV) systems for ventilation, which allow for better part load regulation, reduction of energy consumption and building operation costs, without compromising occupant comfort or safety. But despite all these improvements, most BAS still rely on conventional control methods like rule based on-off control paired with Proportional Integral Derivative (PID) loops, which are single input single output (SISO) models that are not suitable for the complexities of the multivariable requirements of building systems. These outdated strategies have been estimated to annually waste up to 30% of building’s energy. To mitigate these issues the research community has strongly endorsed the use of more advanced and proven effective control methods such as Model-Based Control (MBC), in which abundant work has been done for the supervisory level control like optimal start/stop, setpoint reset scheduling, etc. However, little attention has been given to local level control where PID control remains the chief workhorse of HVAC systems. Mainly because of the difficulties of creating models, as well as the lack of research regarding the implementation of mechanisms required for continuous calibration (also known as adaptability) of model parameters as they start to drift away from their initial values due to system changes or deterioration, which challenges the reliability of any MBC approach. For such reasons the present body of work was conceived to design a practical methodology for a self-adaptive MBC and field data driven approach to improve VAV systems energy efficiency, based on the Total Air Volume (TAV) control method by modifying the shortcomings of its modeling, adaptability and control strategy procedures. Using a regular VAV system inside a high-rise institutional building as an experimental testbed for the proof of concept of this methodology. The results of the test demonstrated that the self adaptive field calibrated TAV method can match and exceed the capabilities of PID control, by improving response time, offset, and above all energy efficiency, were an average 56% of energy consumption was achieved in contrast to the conventional duct static pressure PID control
Occupancy driven supervisory control of indoor environment systems to minimise energy consumption of airport terminal building
A very economical way of reducing the operational energy consumed by large commercial buildings such as an airport terminal is the automatic control of its active energy systems. Such control can adjust the indoor environment systems setpoints to satisfy comfort during occupancy or when unoccupied, initiate energy conservation setpoints and if necessary, shut down part of the building systems. Adjusting energy control setpoints manually in large commercial buildings can be a nightmare for facility managers. Incidentally for such buildings, occupancy based control strategies are not achieved through the use of conventional controllers alone. This research, therefore, investigated the potential of using a high-level control system in airport terminal building. The study presents the evolution of a novel fuzzy rule-based supervisory controller, which intelligently establishes comfort setpoints based on flow of passenger through the airport as well as variable external environmental conditions. The inputs to the supervisory controller include: the time schedule of the arriving and departing passenger planes; the expected number of passengers; zone daylight illuminance levels; and external temperature. The outputs from the supervisory controller are the low-level controllers internal setpoint profile for thermal comfort, visual comfort and indoor air quality. Specifically, this thesis makes contribution to knowledge in the following ways:
It utilised artificial intelligence to develop a novel fuzzy rule-based, energy-saving supervisory controller that is able to establish acceptable indoor environmental quality for airport terminals based on occupancy schedules and ambient conditions.
It presents a unique methodology of designing a supervisory controller using expert knowledge of an airport s indoor environment systems through MATLAB/Simulink platform with the controller s performance evaluated in both MATLAB and EnergyPlus simulation engine.
Using energy conservation strategies (setbacks and switch-offs), the pro-posed supervisory control system was shown to be capable of reducing the energy consumed in the Manchester Airport terminal building by up to 40-50% in winter and by 21-27% in summer.
It demonstrates that if a 45 minutes passenger processing time is aimed for instead of the 60 minutes standard time suggested by ICAO, energy consumption is significantly reduced (with less carbon emission) in winter particularly.
The potential of the fuzzy rule-based supervisory controller to optimise comfort with minimal energy based on variation in occupancy and external conditions was demonstrated through this research. The systematic approach adopted, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns
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