Anomaly Detection in a Reactor Coolant Pump Flywheel System via Pulse Shape Analysis

In this study, a method that discriminates between anomalies present or absent in the vibration signal of a flywheel system is developed. First by means of MATLAB and Simulink, a simple flywheel system under different feasible conditions is simulated using equations of motion to capture the dynamic behaviors of the components of the system along their lines of action. The resulting vibration signals obtained from the simulations are combined with varying levels of noise and then subjected to pulse shape analysis (PSA). PSA is a tool that has been mostly used in the field of nuclear engineering, and it is explored and used differently here with the objective of developing a suitable PSA algorithm that can differentiate between vibration signals based on the presence or absence of an anomaly. The algorithm is a time-domain technique with minimal computational time that can be very easily applied. At the end, it is shown that the developed PSA algorithm can identify an anomaly in a vibration signal on the basis of a defined pattern under certain attainable conditions.     

Performance optimization of integrated gas and power within microgrids using hybrid PSO–PS algorithm

In this paper, a hybrid algorithm consisting of particle swarm optimization and pattern search algorithm is proposed to evaluate and optimize the design and operation of microgrids (MGs) in combined gas and power networks. Key performance indicators (KPIs) are modeled and proposed to evaluate and assess MGs. The paper begins by proposing a comprehensive study to define KPIs, which are used to evaluate the following MG parameters: economical efficiency, reliability, environmental conservation, and power quality. Multi‐objective evaluation functions are then developed by building a relationship matrix of MG and KPI components. The results are then displayed as optimized power generation percentages for each technology with values for four KPI categories: cost, quality, reliability and environmental friendliness. Two case studies are examined in this paper; both the province of Ontario and Toronto regional zone under all system parameters with varying percentage of generation via gas technology. Results indicated that the optimal scenario for both Ontario and Toronto was achieved at hybrid PSO–patern search percentage generation via gas technology with improved cost KPI and other KPIs remaining approximately constant. Copyright © 2016 John Wiley & Sons, Ltd.     

A multi‐objective evolutionary optimization of fuzzy controller for energy conservation in air conditioning systems

This paper presents the use of evolutionary optimization approach to design and tune smart fuzzy controllers for heating, ventilation, and air conditioning systems or HVAC. The objective is to optimize energy consumption while accounting for user comfort requirements. The problem of energy conservation in air conditioning systems becomes a multi‐objective optimization constrained problem, which enlarges the solution search space. To solve this problem, a multi‐objective evolutionary optimization technique based on genetic algorithm (GA) is proposed. A physical experimental setup is constructed for data collection and formulation of mathematical model. A fuzzy controller is initially designed through expert knowledge, and GA is then used to tune the rules and membership functions of the fuzzy controller in order to optimize multiple objectives. Simulations and real experiments are compared to determine the effectiveness of the proposed strategy. As compared to the controller present in the real experimental air conditioner, approximately 15% energy is successfully saved with no increase in average individual dissatisfaction or discomfort index. Also, a decrease in peak individual dissatisfaction or discomfort index from 91% to 62% is observed. Copyright © 2013 John Wiley & Sons, Ltd.     

Design of plant safety model in plant enterprise engineering environment

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 plant's objects. By developing and realizing the plant safety model, safer plant operation can be achieved and plant safety can be assured.     

Modeling Approach for Flexible Production Chain Operation

To meet the dynamically changing market requirements, production enterprises are collaborating in the form of production chains to share life cycle engineering data/knowledge as well as to improve production efficiency and product quality. This paper proposes a model-based control mechanism and intelligent control layer, which is used to control the operation of chained production enterprises. The control mechanism is achieved by defining control levels as mapped to production chain topology using the concept of operation isolation area. Production constraints and control rules are defined as attached to structure elements. The proposed control layer is used to decide the suitable operation of the selected production chain, in view of process constraints and operating conditions. A case study of a generic production chain with recycling is used to show the proposed control mechanism.     

Design of fault simulator

Fault simulator is proposed to understand and evaluate all possible fault propagation scenarios, which is an essential part of safety design and operation design and support of chemical/production processes. Process models are constructed and integrated with fault models, which are formulated in qualitative manner using fault semantic networks (FSN). Trend analysis techniques are used to map real time and simulation quantitative data into qualitative fault models for better decision support and tuning of FSN. The design of the proposed fault simulator is described and applied on experimental plant (G-Plant) to diagnose several fault scenarios. The proposed fault simulator will enable industrial plants to specify and validate safety requirements as part of safety system design as well as to support recovery and shutdown operation and disaster management.     

Detecting nonlinear interrelation patterns among process variables using genetic programming

Detecting non-linear interaction patterns among process variables is an important task for fault detection and propagation analysis. There are many statistical and evolutionary techniques being developed in the literature for prediction of interaction strengths but their accuracy is generally unsatisfactory. This study demonstrates an evolutionary programming approach to uncover non-linear relations among process variables. In this study, we make an attempt to use genetic programming (GP) based approach for this purpose. GP overcomes many shortcomings faced by other statistical or evolutionary techniques in this context. The effectiveness, feasibility, and robustness of the proposed method are demonstrated on simulated data emanating from a well-known Tennessee Eastman process. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.     

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.     

A novel method for real time gear fault detection based on pulse shape analysis

Early identification of faults in gearboxes is a challenging task, especially when the time is a critical factor. In this paper, a novel method for real time fault detection in gearboxes is proposed using adaptive features extraction algorithm to deal with non-stationary faulty signals. Moreover, integration of different techniques is presented in order to detect faults in a real time environment. Evolutionary algorithms are commonly used in different applications and have strong ability for optimization. However, they are inherently slow and not suitable for real time applications. The proposed method is based on a combination of conventional one-dimensional and multi-dimensional search methods, which showed high performance and accurate fault detection results compared with evolutionary algorithms. The effectiveness, feasibility and robustness of the proposed method have been demonstrated on experimental data. An average speed up factor of 87% has been successfully achieved with approximately 5% quality degradation in the results as compared with evolutionary algorithms like genetic algorithms.     

Bond strength of etched-base-metal alloy to enamel using different etching techniques and cements

A comparison of bond strength produced by using three different metal surface treatment were evaluated using three different cements. The surface metal treatment were sandblast, electrochemical etching and chemical etching. The three cements used were ABC, Rely-a-bond and panavia EX cements. The following conclusions were obtained: 1. The tensile bond strength of enamel-resin metal joint with panavia EX resin has the best bond strength when the metal was chemically etched. 2. The debonding strength of the chemically etched joints were superior compared to that obtained with either sandblast or electrochemical etching.