Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

PREDICTIVE ANALYTIC MODEL FOR ELECTRICAL CHILLER SYSTEM (ECC)

District cooling plant is a very complex system consisting of various equipment. One of them is an electric centrifugal chiller that is widely used in the industry to supply chilled water to thermal energy storage (TES). This paper investigates the predictive analytics model of the electric chiller system using real operation data. Data pre-processing was performed to remove the outliers. Further to that, the machine learning model was used to develop an artificial neural network (ANN), whereby the best model with the lowest RMSE was obtained. Then, the ANN was used to correlate between the input and output variables to find the critical parameters which contributed to the Coefficient of Performance (COP). This model could also predict the output from the actual data of the COP. Lastly, the Shewhart control chart was utilised in the root cause analysis model (RCA) to detect the anomalies based on five critical parameters at the early stage before its failure. The supervised learning algorithms using the feedforward ANN model demonstrates the most accurate predictions compare to all models

OPTIMIZATION OF RAW MIX DESIGN OF CLINKER PRODUCTION: A CASE STUDY IN CEMENT INDUSTRY

Raw mix design refers to the raw materials' quantitative proportions to achieve clinker with the desired chemical and mineralogical composition. The existing method used to formulate the raw mix design is based on iterative laboratory trials, which is time-consuming and heavily relies on the chemist's experience. Considering the negative environmental impacts, optimizing the raw mix design has become one of the major concerns among the cement players. Thus, the objective of this research is to optimize raw mix design with minimum cost while satisfying the critical clinker quality control targets. This study explored the Linear Programming (LP) model to achieve the objective. A Series of mathematical modeling was developed to relate the decision variables, raw mix and fuel mix design and the clinker chemistry. Bogue calculation is then applied to correlate the oxides from both raw mix and fuel mix to the phase content of C3S, C2S, C3A and C4AF in the clinker. The ratio of the clinker phases would be Lime Saturation Factor (LSF), Silica Ratio (SR) and Alumina Modulus (AM), which are used to determine the quality of the clinker, were defined as the main constraint. Limitation in the plant design, such as the number of dosing weighers, is also considered programming constraint. A case study was performed with eight types of raw materials consisting of Limestone, clay, sand, alternate material and additives to evaluate the LP model. Based on the GRG Nonlinear LP simulation, the optimized raw mix design was achieved at the cost of RM 6.845 per tonne composed of, 85.03% of Limestone, 0.9% of Clay 1, 12.6% of Alternate Material 1 and 1.47% of Additive 2. The obtained results prove that the developed LP model can minimize the raw material cost save analysis time, and provide flexibility in the raw material selection process without the need for actual trials

Industrial Engineering

Businesses across the world are aiming for increased productivity and greater efficiency. This can be achieved through the knowledge of industrial engineering, which is a systematic approach to streamlining the business process. This book presents the current state of the art of industrial engineering and provides useful information to those who wish to optimize their business practices while increasing customer service and quality

Qad Khaba Man Dassaha

298 hlm., 24 cm

Reliability and Availability Evaluation for a Multi-state System Subject to Minimal Repair

I. Norris #1 well, Pike tap, Coshocton Co., Ohio. (1911)GrayscaleClapp Nitrate Negative, Box 1

Fuzzy Activity Based Life Cycle Costing For Repairable Equipment

Life-cycle cost (LCC) is the much known method used for decision making that considers all costs in the life of a system or equipment. Predicting LCCs is fraught with potential errors, owing to the uncertainty in future events, future costs, interest rates, and even hidden costs. These uncertainties have a direct impact on the decision making. Activity based LCC is used to identify the activities and cost drivers in acquisition, operation and maintenance phase. This activity based LCC is integrated with fuzzy set theory and interval mathematics to model these uncertainties. Day–Stout–Warren (DSW) algorithm and the vertex method are then used to evaluate competing alternatives. A case of two pumps (Pump A and Pump B) are taken and their LCC is analysed using the developed model. The equivalent annual cost of Pump B is greater than Pump A, which leads the decision maker to choose Pump A over Pump B

Design optimization of industrial gas turbines using simulated annealing algorithms

Currently, gas turbine is one of the most widely-used power generating technologies. The race to achieve higher efficiency from gas turbines is gathering momentum with most of the major manufacturers. Cogeneration with advanced engines has the prospect of attaining thermal efficiencies around 60% in the future. In this condition, further development of gas turbine design optimization in order to obtain higher thermal efficiency seems to be beneficial. In the current work, the design of a single shaft gas turbine in a cogeneration plant is optimized based on the model established using thermodynamic theory. The overall thermal efficiency of the engine is tried to be optimized by adjusting the compressor efficiency, turbine efficiency, compression pressure ratio, and turbine inlet temperatures. A feasible solution should satisfy two physical constraints, namely a desired gas turbine power and a suitable limit of engine exhaust temperature. An evolutionary model using Simulated Annealing algorithm is developed to find the sets of optimal solutions in the space defined by user experience and literature. A number of case studies have been performed and an optimal solution and their corresponding performance are discussed