The proposed method provides a good tracking performance through detecting the variation in the wind speed rapidly. In addition, the implementation does not require prior information on the wind turbine parameters, air density, or wind speed. By investigating the change directions of the mechanical output power of the wind turbine and the rotor speed of the generator, the proposed MPPT algorithm can determine an optimal speed to achieve the maximum power point (MPP).

Then, this optimal speed is set to the reference in the speed control loop to force the system to operate at the MPP. The DC-DC boost converter has been used to boost up the low AC voltage generated by the PMSG directly driven by the wind turbine. The mechanical output power of the wind turbine is calculated from the mechanical torque which is measured directly by sensor.

The proposed scheme is modeled and simulated under SIMULINK/MATLAB. The simulation results are accurate and validate the wind energy conversion system.

Keywords: Wind Turbine, maximum power point tracking (MPPT), permanent magnet synchronous generator (PMSG), wind energy conversion system (WECS).

the purpose of this paper is to use discrete event simulation to model breakdowns of filling machines for food manufacturing line. It presents an approach to model breakdowns based on purely historical data where MTBF and MTTR have been extracted in order to replicate the existing machine and the breakdown occurrences. The historical data gathered from the operations department were first analysis over a period of 28 months in order to extract the Failure and repair distributions of machines; these statistical distributions were applied in the first model.

The data were also used to calculate the components of the OEE of the filling line to identify potential areas of improvement.

The models were built using Arena Simulation Package; the simulation used to represent the filling line machines with a series of elements and built in templates in addition to animation tools to improve the display at the human interface. Simulation enables the replicating of the business model in a computer system totally risk free, as you can apply and make changes to the model, create “what if” scenarios to test strategic changes, and extract results.

The purpose of this paper is to minimize the total cost of raw material inventory for

water desalination and bottling plant resulting in a more profitable approach in

accordance with the production needs. The classical application of Economic Order

Quantity is used to support this paper and in order to reduce the costs related to

inventory. For this purpose, this method generates a minimum total inventory cost by

finding when ordering cost and carrying cost are equal. From this result, the inventory

level and the number of raw material demand become more economically suitable with

the production needs. It is because this method applies two types of cost, carrying cost

and ordering cost that make the total inventory cost more productive. So, this method

can be used to get the most economical total inventory cost and reduce storage cost

swelling. And hence, decreasing costs means that more profit is achieved.

World over the air travel is the fastest growing source of greenhouse gases leading towards global climate change. Estimates show that each long distance flight of a 747 adds approximately 400 tons of CO₂ to the atmosphere. Like elsewhere in Libya too with the expected threefold increase in air travel over the next 30 years, the reliability and environmental impact of aviation are becoming critical issues for the future of flight in the country.

This paper is designed to present greener sky initiative indicating how Civil Aviation Technical College based at Sbia Libya in its march towards sustainable aviation is focusing on critical technologies like: (a) Integrated modeling of vehicles, atmospheric impact, and noise (b) optimization of new aircraft concepts and operations including environmental impact and (c) distributed, adaptive sensing and control. It treats sustainability is the way forward for aviation to …

This paper demonstrates a new approach in solving problems with capacity issues within a cement factory in Libya using the Witness simulation package that enables the development of discrete and continuous simulation model to simulate these processes. The model aims to support the implementation of management systems in Supply Chain Management and the introduction of Just-In-Time systems. In order to carry out the above, research has been carried out into processes of SCM and J.I.T. The developed Simulation model is based on Libyan Cement industry, exploring supply chain management (SCM) and Just IN Time (JIT) system from start to end of the processes. Therefore, the aim and objective of investigating supply chain management and applying JIT to the cement factory using simulation at the very stage has been successful and has exercised with the simulation scenarios 1,2 and 3. This was achievable simply based on many assumptions as addressed above and with the help of the witness software that executes duties with the aid of input and output rules that need to be applied accordingly.

Toyota, in Japan started the creation of this method in the early 1960's and hence it is also known as the “Toyota Production System” as it was used for their production of their automobiles. Therefore, the best definition of this system comes from Toyota i.e. JIT is a disciplined programme for improving overall productivity and reducing waste. It provides for the cost-effective production and delivery of only the necessary required parts, in the right quantity, at the right time and place, while using a minimum amount of facilities, equipment, materials and human resources. JIT is dependent upon the balance between the supplier's flexibility and user's stability. It is accomplished through the application of specific techniques that require total employee involvement and teamwork.

This paper considers a common problem that all industries contest with in practice ie the breaking down of machines that influence production and cost directly. In a majority of industrial applications, acquiring optimum utilisation of all available resources for existing and future predicted demand is a major function throughout all the levels of management. This paper uses a number of analytical tools and software that support one another ie Witness Simulation, Bayesian Network Modelling and Hugin Software. The use of expert experience and knowledge has been incorporated throughout the study as it is vital to model building and greater understanding of machine breakdown. This study uses discrete event simulation and Bayesian network modelling collectively to understand machine breakdowns to increase efficiency within a cement manufacturing plant ie the Crusher Machine. The Bayesian network modelling implemented by the Hugin Software is used to generate probabilities which are transferred into a discrete event simulation model using Witness Software based on the historical data, expert knowledge and opinions. The model simulates the three parameters of the machine life based on consumption of each parameter. This is translated into a probability failure rate that changes as the model is running. The model demonstrates decisions based on the probability of failure from the Bayesian model and based on life consumption of the different variables in the simulation model.

This paper looks into how simulation can be used and developed to combat and aid integrated maintenance and cost optimisation of Libyan cement factory by using simulation as an intelligent maintenance tool to enable intelligent decisions to be made by maintenance management. The cost of preventive maintenance against corrective maintenance, the loss of production and the delay in schedule. Also this paper presents a methodology by integrating maintenance, repair and replacement decisions in bridge management based on reliability, optimization, and life-cycle cost. The overall total cost incurred per minute due to machine failure for more intelligent maintenance decision making. The end result of the model identifies a true to life cost of preventive and corrective maintenance incurred by organisation due to failures.

This paper presents a new approach using Witness Simulation in combating issues related to maintenance within the existing manufacturing facility. Currently, maintenance is a key issue as many machines breakdown due to every day wear and tear as well as the age of the machine itself. This causes bottlenecks within the entire production system as the schedules of production is disturbed and further backlogs are developed. Also, when machines breakdown there seems to be very little communication from one end to another ie parts are needed for the repair of many of the machines, for example, it is very common for the rotary kiln to come to a halt due to increased temperature and different quantities of a mixture of materials. In order to fix the problem and move back on schedule the maintenance team uses certain materials ie same materials, all the time. However, due to not having an adequate stock control system and the lack of communication many a times, parts are not readily available and hence the team wait further until parts arrive at the facility. This paper will demonstrate how all of the above problem areas can be adhered to via the use of adopting a simple automated response system using Witness Simulation. The demonstration will be based on a single machine for testing purposes to ensure efficiency that can later be applied to the entire system as a whole.

This paper explores the use of Bayesian network modeling of machine breakdowns within a cement manufacturing plant. The Bayesian network modeling is introduced using Hugin software and then implemented into a Witness Simulation model using historical data, expert knowledge and opinions. The models simulate 3 parameters of the machine based on life consumption and usage of each parameter, the developed Witness model produces a probability failure rate based on these parameter usages. The failure probability developed by Witness is implemented using the Chain Rule; this is compared with the probability for failure based on the Bayesian network model. This enables the user to see the probability of failure developed by the implementation of the chain rule and Witness based on the parameter usage, on a live streaming fashion as the model is running. This can be used as a decision making tool for management to consider machine maintenance affectively based on parameter usage.

This paper explores the value of stochastic simulation as a tool for predicting current or future reliability of machinery, it helps to understand machine failures by using confidence levels developed through replications and exploring the changes that occur. A stochastic simulation model has been constructed representing a crusher machine with in a cement manufacturing plant. The crusher machine has three parameters; namely those are drill head, dusting and lubrication. The consumption of these parameters results in the development of a probability of failure for the machine using Bayes theory.

Due to the inherent complexity of decision making in supply chain management (SCM), there is a growing need for modelling supply chain systems with different methodologies. A large number of manufacturing organisations in particular the cement industry in Libya is seeking techniques that can help, identify and implement strategies for developing and improving their supply chain network. Developing a supply chain management system requires the analysis of the manufacturing processes from the initial sourcing to the end customers. The paper reports that the Libyan cement industry has problems related to its productivity. In order to face these problems, cement factories need to increase their production. To accomplish this, they require a clear strategy towards an efficient supply chain.