Dr. Mohamed Gamal Eldin Abd ElMonem

This paper looks at industrial disaster from the perspective of resilience engineering, which is a new paradigm in safety management, where “success” is based on the ability of organizations, groups and individuals to adjust their functioning prior to, during, or following changes, disturbances, and opportunities so that it can sustain required operations under both expected and unexpected conditions. A new vision of resilience engineering as an intelligent control system is first developed and… عرض المزيد

This paper looks at industrial disaster from the perspective of resilience engineering, which is a new paradigm in safety management, where “success” is based on the ability of organizations, groups and individuals to adjust their functioning prior to, during, or following changes, disturbances, and opportunities so that it can sustain required operations under both expected and unexpected conditions. A new vision of resilience engineering as an intelligent control system is first developed and then applied to the analysis of the industrial disasters. This paper suggest that the failures that leads to industrial disasters are basically a failure of the resilience engineering control system. عرض أقل

The need to safely and reliable operation of petroleum facilities beyond their expected lifetime is of paramount importance and forms a key part of any strategy to manage current and future business risk. However operating such facilities without considering the degradation of the plant equipment and its compliance to changes in safety codes and standards could be risky. The issue of ageing plant, leading to an increased risk of loss of containment and other failures due to plant and equipment… عرض المزيد

The need to safely and reliable operation of petroleum facilities beyond their expected lifetime is of paramount importance and forms a key part of any strategy to manage current and future business risk. However operating such facilities without considering the degradation of the plant equipment and its compliance to changes in safety codes and standards could be risky. The issue of ageing plant, leading to an increased risk of loss of containment and other failures due to plant and equipment deterioration, has been shown to be an important factor in incidents and accidents.
In this paper a methodology for assets integrity management of petroleum facilities beyond their expected life time from the point of view of E/C&I is introduced. The introduced methodology will focus on safety considerations and layer of protection analysis. عرض أقل

Oil price fluctuation potentially has significant effects on the economic performance of industrial plants. So it is important to incorporate oil price forecasting into the enterprise resource planning system of these oil industrial plants.
This paper proposes a novel prediction model based on Softcomputing methodologies and just on time learning (JITL) to predict oil price. Softcomputing methodologies are a set of techniques including genetic algorithms, neural networks, and fuzzy logic… عرض المزيد

Oil price fluctuation potentially has significant effects on the economic performance of industrial plants. So it is important to incorporate oil price forecasting into the enterprise resource planning system of these oil industrial plants.
This paper proposes a novel prediction model based on Softcomputing methodologies and just on time learning (JITL) to predict oil price. Softcomputing methodologies are a set of techniques including genetic algorithms, neural networks, and fuzzy logic. These techniques had proved that they are capable of efficiently modeling nonlinear and chaos processes. The JITL based method exhibits an on line local model structure, on which the changes of the oil price can be well tracked. Besides the price non linearity can also be addressed under this modeling framework. Incorporating the proposed model into the Enterprise resource planning system could be economical since the process of strategic decision making will be fully automated. Empirical results obtained demonstrate attractiveness of the proposed model. In addition, comparisons between the proposed model and other models are conducted. It is shown that for several randomly selected durations, the proposed model predictions are considerably higher than the result of most recent published prediction algorithms. عرض أقل

In process engineering, detailed models of individual units are used to determine the trade-offs between throughput, energy and yield. In addition, advanced process control (APC) models employ linear and nonlinear programming techniques. These models utilize the relationships between manipulated variables and process responses together with feed and product prices to optimize operation locally at the process unit. These models have good representation of equipment capacities, but poor or… عرض المزيد

In process engineering, detailed models of individual units are used to determine the trade-offs between throughput, energy and yield. In addition, advanced process control (APC) models employ linear and nonlinear programming techniques. These models utilize the relationships between manipulated variables and process responses together with feed and product prices to optimize operation locally at the process unit. These models have good representation of equipment capacities, but poor or limited economic capability due to a focus narrowed to the particular process unit. Adding economic capability to those models will be profitable. Optimization could be conducted according to profit margins for each product that may be produced. The profit margin calculation is determined based on current market prices versus manufacturing cost. However enterprise resource planning system requires planning for months which require forecasting oil and product prices.

This paper proposes a novel prediction model based on Softcomputing methodologies to predict oil price. Softcomputing methodologies are a set of techniques including genetic algorithms, neural networks, and fuzzy logic. These techniques had proved that they are capable of efficiently modeling nonlinear and chaos process. The inputs of the proposed Softcomputing oil price predictor (SCOPP) model are past oil and gold prices in addition to the Dow Jones Industrial Average. The proposed SCOPP model predicts oil prices for the next few months. Incorporating the proposed SCOPP model into the Enterprise resource planning system could be economical since the process of strategic decision making will be fully automated. عرض أقل

Industrial plants are being increasingly required to improve their production efficiency while respecting tight limits on product specifications and on pollutant emissions. It is important to monitor a large set of process variables using adequate measuring devices. Some required measurements are either too difficult or too expensive to measure in real time. These measurements could be calculated using soft sensors. Soft sensors are mathematical models of processes, designed on the basis of… عرض المزيد

Industrial plants are being increasingly required to improve their production efficiency while respecting tight limits on product specifications and on pollutant emissions. It is important to monitor a large set of process variables using adequate measuring devices. Some required measurements are either too difficult or too expensive to measure in real time. These measurements could be calculated using soft sensors. Soft sensors are mathematical models of processes, designed on the basis of experimental data, via system identification procedures where several easily measured variables are processed together to calculate an estimated or predicated measurement. They are used to solve a number of different problems such as measuring system back-up, what-if analysis, real-time prediction for plant control, sensor validation and fault diagnosis strategies. The main issue in soft sensor design is using efficient identification procedures capable of modeling nonlinear process. Here come Softcomputing methodologies which are a set of techniques including genetic algorithms, neural networks, and fuzzy logic. These techniques had proved that they are capable of efficiently modeling nonlinear process.
This paper proposes basic steps to develop a soft sensor based on soft computing methodologies. In addition, it proposes a new robust soft computing soft sensor builder RSCSSB. The proposed RSCSSB is tested using a benchmark problem of sulfur recovery unit.
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In this paper, a neuro TSK new fuzzy reasoning model (NSNFRM) is proposed. The proposed model uses the antecedent part of the neuro-new fuzzy reasoning model (NNFRM) proposed by the authors in Mazhar Tayel et al. [2000] and the consequent part of the TSK fuzzy model. The proposed model combines the advantage of the NNFRM and the advantage of the TSK fuzzy model. The advantages of NNFRM is first, using a neural network to represent the fuzzy model and second, using small number of rules and… عرض المزيد

In this paper, a neuro TSK new fuzzy reasoning model (NSNFRM) is proposed. The proposed model uses the antecedent part of the neuro-new fuzzy reasoning model (NNFRM) proposed by the authors in Mazhar Tayel et al. [2000] and the consequent part of the TSK fuzzy model. The proposed model combines the advantage of the NNFRM and the advantage of the TSK fuzzy model. The advantages of NNFRM is first, using a neural network to represent the fuzzy model and second, using small number of rules and inputs. The advantage of TSK fuzzy model is getting lower mean square error. The parameter of the proposed model is tuned by a proposed hybrid genetic-recursive least squares (RLS) learning algorithm in which the antecedent parameters are tuned by the genetic algorithm and the consequent parameters are tuned by RLS algorithm. The performance of the proposed model is evaluated using a benchmark problem and compared with other modeling methods. It is shown that the proposed model outperforms other modeling methods including the standard TSK fuzzy model and the NNFRM model. عرض أقل

In this paper, a genetic algorithm with adaptive probabilities of crossover and mutation is introduced to find near global optimum parameters for the Neuro-new fuzzy reasoning model (NNFRM). The parameters to be optimized are those of input membership functions, output membership functions and relation matrix. A fuzzy evaluation criterion is introduced to evaluate the different fuzzy models. This criterion stresses the fact that the fuzzy system must be comprehensible and transparent to the… عرض المزيد

In this paper, a genetic algorithm with adaptive probabilities of crossover and mutation is introduced to find near global optimum parameters for the Neuro-new fuzzy reasoning model (NNFRM). The parameters to be optimized are those of input membership functions, output membership functions and relation matrix. A fuzzy evaluation criterion is introduced to evaluate the different fuzzy models. This criterion stresses the fact that the fuzzy system must be comprehensible and transparent to the user. The performance of the proposed model is evaluated using a benchmark problem. Also, the generalization of the proposed model is compared to the feed forward neural network. It is shown that the proposed GNNFRM outperforms other modeling methods. The generalization of the proposed model is better than that of the feed forward neural network عرض أقل

An interpretation of the new fuzzy reasoning model (NFRM) is developed. This interpretation makes the traditional fuzzy reasoning model (FRM) a special case under certain conditions. In addition, a neural network is constructed to represent the NFRM. The proposed neuro-new fuzzy reasoning model (NNFRM) optimizes the parameters of the NFRM by using the well-known backpropagation concept. The parameters to be optimized are those of the input membership functions, output membership function and… عرض المزيد

An interpretation of the new fuzzy reasoning model (NFRM) is developed. This interpretation makes the traditional fuzzy reasoning model (FRM) a special case under certain conditions. In addition, a neural network is constructed to represent the NFRM. The proposed neuro-new fuzzy reasoning model (NNFRM) optimizes the parameters of the NFRM by using the well-known backpropagation concept. The parameters to be optimized are those of the input membership functions, output membership function and relation matrix. The proposed NNFRM is used to predict future values of a chaotic time series, which is considered a benchmark problem. It is shown that the proposed NNFRM outperforms other modeling methods in prediction of this chaotic time series. The NNFRM used here has fewer adjustable parameters, than those used in other modeling techniques عرض أقل

If a software system can be structured as a collection of largely independent subtasks, significant reduction in elapsed time can be realized by executing these subtasks in parallel on multiple processors. The total amount of processor idle time increases with the number of processors; due to factors such as contention for shared resources, intercommunication, and software structure. In this paper the inherent parallelism of a software system is investigated. A new definition for the partial… عرض المزيد

If a software system can be structured as a collection of largely independent subtasks, significant reduction in elapsed time can be realized by executing these subtasks in parallel on multiple processors. The total amount of processor idle time increases with the number of processors; due to factors such as contention for shared resources, intercommunication, and software structure. In this paper the inherent parallelism of a software system is investigated. A new definition for the partial average parallelism is introduced. Using this definition two analytical expressions are developed to compute the minimum number of processors executing a parallel software at maximum obtainable speedup, and to compute the minimum time to execute a software in a fixed number of processors. The presented example shows that these two expressions are extensively useful when choosing the optimal schedule algorithm. The exact location of the knee (the point where the benefit per unit cost is maximized) is very important in multiprogramming environment where maximum efficiency is required. An expression for the number of processors at the knee is also deduced. A computer program is given that calculates the minimum number of processors, the minimum time, and the exact location of the knee عرض أقل