Alternative sweetener from curculigo fruits

This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes

Networked control system for electrohydraulic flow control positioner using Neural Controller and Collaborative Network

Electrohydraulic flow control valve is an essential element of an automated process industry where fluid control is applicable. The use of conventional controllers overan IP-communication network for controlling electrohydraulic flow control positioner to regulate mainline pressure and flow rate in pipeline transportation of petroleum products between two stations where downstream pressure of the pumping station fluctuates significantlyposes a problem of instability on the flowrate and the mainline pressure of the pipeline. Additionally, the effect of network induced, time-varying delay between the controller and the electrohydraulic flow control valves induces a problem of poor quality of control and inefficient system performance of the control loop. In this paper, we presented an application of neural network in processflow control using an electrohydraulic valve positionerand proposed a concept of collaborative network for networked control systems over IP-based networks.peer-reviewe

PLC – HMI Automation Based Cascaded Fuzzy PID for Efficient Energy Management and Storage in Real Time Performance of a Hydro Electric Pumped Storage Power Plant

AbstractUsually in order to balance the load demand at high peak hours, hydro electric pumped storage power plant is utilized. In this project a novel design, (CFPID) Cascaded Fuzzy PID (Proportional - Integral - Derivative) controller scheme using B&R (Bernecker & Rainer) Industrial Automation PLC - HMI (Programmable Logic Control - Human Machine Interface) for Efficient Energy management and storage in real time performance of a hydro electric pumped storage power plant is proposed. In this scheme, Fuzzy Level is cascaded with PID Flow for improvement in performance. A prototype model of a hydro electric pumped storage power plant with 22 digital inputs and 14 potential free outputs is fabricated with an objective of controlling the process variables, flow and level is done by using conventional PLC and the proposed CFPID control. There are two tanks in the prototype, lower tank with 2 stages of level and upper tank with 5 stages of level. HMI is used to monitor and operate the process in online - real time, for easy control of the operations. In this paper, the proposed PLC- HMI automation based CFPID control scheme is performed and finally compared with the conventional PLC by experimental results and validated by using real time statistics obtained from the hydro electric pumped storage power plant

NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

AN EXTENSION OF THE FAILURE MODE EFFECTS AND CRITICALITY ANALYSIS WITH FUZZY ANALYTICAL HIERARCHY PROCESS METHOD TO ASSESS THE EMERGENCY SAFETY BARRIERS

The emergency safety barrier is one of the reactive technical safety barriers in industrial facilities. Degrade of emergency safety barriers can lead to a major accident with serious consequences for people, property and the environment. In this context, the purpose of this article is to present a proposed methodology to identify these deficiencies, thus ensuring the effectiveness of the emergency safety barriers. This paper presents an integrated approach that uses fuzzy set theory, extension of failure modes, effects and criticality analysis and the fuzzy analytic hierarchy process method to deal with uncertainty in decision-making related to the prioritization of risk factors. These risk factors are the prioritization of corrective actions associated with the most critical disturbance modes to improve the reliability of emergency safety barriers. In addition, a Liquefied Petroleum Gas production facility was selected as a case study to assess the emergency safety barriers. The results show that the proposed methodology provides the possibility to evaluate the fire-fighting systems. In addition, the fuzzy analytical approach method is the most reliable and accurate. Therefore, some corrective actions are suggested to reduce the failure criticality of the emergency safety barriers and help practitioners prioritize the improvement of the emergency safety barriers of the Liquefied Petroleum Gas storage facility. This paper has an important role in the dysfunctional analysis of the emergency safety barriers related to the others effects of the release of LPG, such as the effects of domino scenarios

HAZOP Analysis in Terms of Safety Operations Processes for Oil Production Units: A Case Study

The Hazard and Operability Study (HAZOP) methodology is considered one of the most effective techniques for risk analysis, developed fundamentally to provide regular processes with reduced risks that aim to guarantee the safety of activities and the operability of the production units. The study aims to apply the HAZOP methodology in process and safety operations in the oil production industry. A crude oil production unit was divided into smaller sections that were analysed. By applying the HAZOP methodology, 71 possibilities of relevant risks were identified. The environmental, health and economic impacts were estimated to establish safeguard priorities for them. The application of this methodology and the defined safeguards generated 47 recommendations to mitigate the detected problems. The study contributions were to demonstrate the efficacies of HAZOP methodology to identify potential hazards and evaluate the potential hazards obtained for malfunctioning of equipment and property in terms of the resultant impacts either new or existing process facilities, and as a useful tool to provide essential knowledge for the companies' leaders, decision-maker, and operations managers

A study of the design expertise for plants handling hazardous materials

A study of the design expertise for plants handling hazardous material

Optimization of maintenance performance for offshore production facilities

Master's thesis in Offshore technologyNew technologies are becoming advanced and complex for offshore production facilities. However this advancement and complexity in technology creates a more complicated and time consuming forensic processes for finding causes of failure, or diagnostic processes to identify events that reduce performance. As a result, micro-sensors, efficient signaling and communication technologies for collecting data efficiently, advanced software tools (such as fuzzy logic, neural networks, and simulation based optimization) have been developed, in parallel, to manage such complex assets. Given the nature and scale of ongoing changes on complexities, there are emerging concerns that increasing complexities, ill-defined interfaces, unforeseen events can easily lead to serious performance failures and major risks. To avoid such undesirable circumstances, „just-in-time‟ measures of performance to ensure fully functional is absolutely necessary. The increasing trend in complexity creates a motivation to develop an integrated maintenance management framework to get real-time information to solve problems quickly and hence to increase functional performance (help the asset to perform its required function effectively and efficiently while safeguarding life and the environment). Establishing “just-in-time” maintenance and repairs based on true machine condition maximizes critical asset useful life and eliminates premature replacement of functional components. This thesis focuses on developing an integrated maintenance management framework to establish „just-in-time‟ maintenance and to ensure continuous improvements based on maintenance domain experts as well as operational and historic data. To do this, true degradation of components must be identified. True level of degradation often cannot be inferred by the mere trending of condition indicator‟s level (CBM), because condition indicator levels are modulated under the influence of the diverse operating context. Besides, the maintenance domain expert does not have a precise knowledge about the correlation of the diverse operating context and level of degradation for a given level of condition indicator on specific equipment. Efforts have been made in here to identify the true degradation pattern of a component by analyzing these vagueness and imprecise knowledge. Key words: effective and efficient maintenance strategy, ‘just-in-time’ maintenance, condition based maintenance, P-F interval