Hossam A. Gabbar

Computer-aided RCM-based plant maintenance management system

In most of the industries, classical reliability-centered maintenance (RCM) is employed to decide the maintenance strategies using reliability data without having adequate interaction with the design and operational systems. This means that the RCM process will be conducted with no or limited access to the design and operational data/knowledge. Commonly, the developed maintenance strategies are implemented and managed within the computerized maintenance management system (CMMS), which is usually separate from the RCM automated environment. This paper presents the detailed system design and mechanism of improved RCM process as integrated with CMMS. The proposed solution is integrated with design and operational systems and consolidates some successful maintainability approaches to formulate an effective solution for optimized plant maintenance. The major components of the enhanced RCM process are identified and a prototype system is implemented as integrated with the various modules of the adopted CMMS (MAXIMOt). A case study is used to show the effectiveness of the proposed RCM-based CMMS solution in optimizing plant maintenance over the traditional approaches. r 2003 Elsevier Ltd. All rights reserved.

A hybrid statistical genetic-based demand forecasting expert system

Demand forecasting is considered a key factor for balancing risk of over-stocking and out-of-stock. It is the main input to supply chain processes affecting their performance. Even with much effort and funds spent to improve supply chain processes, they still lack reliability and efficiency if the demand forecast accuracy is poor. This paper presents a proposal of an integrated model of statistical methods and improved genetic algorithm to generate better demand forecast accuracy. An improved genetic algorithm is used to choose the best weights among the statistical methods and to optimize the forecasted activities combinations that maximize profit. A case study is presented using different product types. And, a comparison is conducted between results obtained from the proposed model and from traditional statistical methods, which demonstrates improved forecast accuracy using the proposed model for all time series types.

Engineering design of green hybrid energy production and supply chains

There is a national and international move towards green energy production and supply chains. This requires a systematic engineering design approach that enables government and private energy producers and agents to design and operate the target green hybrid energy production chains in flexible and optimized manner. This research paper presents analytical view and process modeling and engineering design framework to design and evaluate green hybrid energy production / supply chains. Process models are constructed on the basis of process object oriented modeling methodology, or POOM. Performance indicators are evaluated in different hierarchical levels using risk-based life cycle and environmental assessment framework, which is essential to evaluate different energy production chain scenarios based on risk and environmental perspectives. Case study is illustrated to explain the proposed engineering design of energy production chains, which is evaluated using developed computer-aided process engineering environment.

Intelligent topology analyzer for improved plant operation

Almost all process engineering practices are tightly linked with process topology. In order to support plant life cycle activities, it is essential to provide intelligent topology analyzer that can intelligently partition plant topology and define topology areas for the different plant operation tasks and activities. This paper presents design of intelligent plant topology analyzer which is integrated with plant automation systems, and used to support the different operational views. The proposed topology analyzer is based on robust plant process modeling methodology, called POOM, or plant/process object oriented modeling methodology, which is used to model process design and plant topology. Plant domain knowledge structure is proposed and used to analyze the underlying plant topology in view of various engineering requirements, i.e. operation design and safety management. Different views for plant topology analysis are explained using HDS plant process as a case study

Fault diagnosis in gearbox using adaptive wavelet filtering and shock response spectrum features extraction

A wavelet adaptive filtering technique is presented for enhanced fault identification in gearboxes. Based on Morlet wavelet analysis and conventional optimization methods, an adaptive filtering is performed for the background noise removal of vibration signals emanating from gearboxes. A fourth-order statistical moment, kurtosis, is used as an objective function to optimize. A filtered signal is obtained by choosing the suitable Morlet wavelet that maximizes the kurtosis. The optimization framework uses one-dimensional and multidimensional accelerated search techniques to speed up the convergence in solution search space. A novel, transient-based features extraction method based on the shock response spectrum is used to extract characteristic features representing the health state of the gearbox. The effectiveness and feasibility of the proposed method have been demonstrated on experimental gearbox data. The proposed technique enables a high signal-to-noise ratio for gearbox fault detection.

Experiment on distributed dynamic simulation for safety design of chemical plants

Abstract To meet the market challenges, chemical plants need to provide safer plant operation. Safety design approach is used to ensure the safety during the design stage, which satisfies the safety during the plant operation. In such approach, simulation practices are widely used to provide quantitative measures to assess the fault propagation and abnormal situations. As most of the chemical plants are becoming more complex, simulation tools need to provide more intelligent and distributed environment to meet the performance requirements. This research work proposes a distributed simulation environment to support safety design activities of complex chemical plants using intelligent agents. The proposed approach is based on dividing the complex plant design model into smaller and controlled sub-processes called control group units (CGUs). The design and simulation activities of each CGU will be carried out independently on a set of computation resources. Intelligent agents are developed to integrate the plant partitions where simulation results are exchanged among the different simulation sessions. The proposed distributed simulation environment is used to assess the fault propagation within each plant segment (i.e. CGU) and between the adjacent CGUs. The proposed solution is used during the design of a case study HDS chemical plant.

Design of plant safety model in plant enterprise engineering environment

Abstract Plant enterprise engineering environment (PEEE) is an approach aiming to manage the plant through its lifecycle. In such environment, safety is considered as the common objective for all activities throughout the plant lifecycle. One approach to achieve plant safety is to embed safety aspects within each function and activity within such environment. One ideal way to enable safety aspects within each automated function is through modeling. This paper proposes a theoretical approach to design plant safety model as integrated with the plant lifecycle model within such environment. Object-oriented modeling approach is used to construct the plant safety model using OO CASE tool on the basis of unified modeling language (UML). Multiple views are defined for plant objects to express static, dynamic, and functional semantics of these objects. Process safety aspects are mapped to each model element and inherited from design to operation stage, as it is naturally embedded within plants objects. By developing and realizing the plant safety model, safer plant operation can be achieved and plant safety can be assured.

Fault semantic networks for accident forecasting of LNG plants

In order to reduce risks associated with Liquefied Natural Gas (LNG) production facilities, one approach is to provide real time and risk-based accident forecasting mechanisms and tools that will enable the early understanding of process deviations and link with possible accident scenarios. In this paper, process and fault modeling technique is presented to model causation models and link with accident scenarios using fault semantic networks (FSN). A forecasting algorithm is developed to identify and estimate safety measures for each operation step and process model element and validated with process condition.

Virtual Plant Design for Future Production Management

Operator is a key player in plant operation. However, still operator working environment is limited to traditional interfaces and monitoring systems, which include actual plant, sensors, alarms, and other process and operation condition monitoring systems. Providing operator with virtual environment that integrates plant and process conditions in actual and virtual modes will support operator decisions in normal and abnormal situations. This research work discusses current limitations and proposes future generation virtual plant environment that enables operator to comprehend current plant and process condition and predict future states for safe and optimum operation. To achieve such target, process modeling is proposed to analyze operator activities in view of process design and operation practices

Resilient micro energy grids with gas-power and renewable technologies

The world is moving towards smart energy grid with green and clean infrastructure which will enable efficient bidirectional energy supply with reduced carbon footprint. Due to increasing energy demands and the pressing issues of efficient energy use, there is a real need to increase the penetration of gas technologies in the power grid. The government of Canada and stakeholders are looking for ways to increase the reliability and sustainability of the power grid; and gas-power technologies may provide a solution. This paper explores the integration of gas and renewable energy generation technologies within various electricity generation scenarios with the goal of developing designs for a resilient micro energy grid (MEG). The distinct scenarios are then evaluated using an advanced algorithm to provide optimum scenario depending on various key performance indicators (KPIs). KPIs to be examined include: economic, power quality, reliability, and environmental friendliness. This work is done using three different systems; geographic information system (GIS) for recording transmission/distribution lines and generation data, a database to store the information, and a MATLAB-based algorithm for evaluating scenarios. These systems are synthesized and represented into a graphical user interface (GUI), where the user defines the zone, area and cell for desired output and system parameters to generate distinct scenarios to identify the optimum generation.