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

Robust control of room temperature and relative humidity using advanced nonlinear inverse dynamics and evolutionary optimisation

A robust controller is developed, using advanced nonlinear inverse dynamics (NID) controller design and genetic algorithm optimisation, for room temperature control. The performance is evaluated through application to a single zone dynamic building model. The proposed controller produces superior performance when compared to the NID controller optimised with a simple optimisation algorithm, and classical PID control commonly used in the buildings industry. An improved level of thermal comfort is achieved, due to fast and accurate tracking of the setpoints, and energy consumption is shown to be reduced, which in turn means carbon emissions are reduced

A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection

Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision

Enhancing fuel cell lifetime performance through effective health management

Hydrogen fuel cells, and notably the polymer electrolyte fuel cell (PEFC), present an important opportunity to reduce greenhouse gas emissions within a range of sectors of society, particularly for transportation and portable products. Despite several decades of research and development, there exist three main hurdles to full commercialisation; namely infrastructure, costs, and durability. This thesis considers the latter of these. The lifetime target for an automotive fuel cell power plant is to survive 5000 hours of usage before significant performance loss; current demonstration projects have only accomplished half of this target, often due to PEFC stack component degradation. Health management techniques have been identified as an opportunity to overcome the durability limitations. By monitoring the PEFC for faulty operation, it is hoped that control actions can be made to restore or maintain performance, and achieve the desired lifetime durability. This thesis presents fault detection and diagnosis approaches with the goal of isolating a range of component degradation modes from within the PEFC construction. Fault detection is achieved through residual analysis against an electrochemical model of healthy stack condition. An expert knowledge-based diagnostic approach is developed for fault isolation. This analysis is enabled through fuzzy logic calculations, which allows for computational reasoning against linguistic terminology and expert understanding of degradation phenomena. An experimental test bench has been utilised to test the health management processes, and demonstrate functionality. Through different steady-state and dynamic loading conditions, including a simulation of automotive application, diagnosis results can be observed for PEFC degradation cases. This research contributes to the areas of reliability analysis and health management of PEFC fuel cells. Established PEFC models have been updated to represent more accurately an application PEFC. The fuzzy logic knowledge-based diagnostic is the greatest novel contribution, with no examples of this application in the literature

Control of HVAC system comfort by sampling

The sampling of the users comfort, allows observing and predicting the level of comfort on the HVAC (heating, ventilation, and air conditioning) systems. The development of online sampling systems assists in the recognition of the behavior patterns that occur in the offices. This paper presents a user-friendly tool designed and developed in order to make easier knowledge extraction and representation to make possible decisions about which demand that must prevail, the user comfort or saving into a central system. This decision may depend on the occupation and feeling of comfort of its occupants. Some studies have put neutral thermal conditions outside the ranges of comfort of the ASHRAE standard. The actual rules of the HVAC systems are based on studies carried out on specific populations in a specific space, which are not valid in certain situations. This is a dynamic idea of the comfort based in real data. The methodology used provides important and useful information to be able to select the comfort set-point of the rooms of a central heating system without the need to use fixed values based on programmed time schedules or any other methodology. The response to comfort in an area of a building throughout the day can be seen in this study. The users were assessed using a standard set of key questions in order to measure the level of satisfaction with environmental factors, thanks to a questionnaire of imprecise answers. We seek an improvement in the building users, regardless of their particularities

Designing an occupancy flow-based controller for airport terminals

One of the most cost-effective ways to save energy in commercial buildings is through designing a dedicated controller for adjusting environmental set-points according occupancy flow. This paper presents the design of a fuzzy rule-based supervisory controller for reducing energy consumptions while simultaneously providing comfort for passengers in a large airport terminal building. The inputs to the controller are the time schedule of the arrival and departure of passenger planes as well as the expected number of passengers, zone global illuminance (daylight) and external temperature. The outputs from the controller are optimised temperature, airflow and lighting set-point profiles for the building. The supervisory controller was designed based on expert knowledge in MATLAB/Simulink, and then validated using simulation studies. The simulation results demonstrate significant potential for energy savings in the controller's ability to maintain comfort by adjusting set-points according to the flow of passengers. Practical application : The systematic approach adopted here, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns like the restricted area of the airport terminal building

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms