KNOWLEDGE-BASED APPROACH FOR THE FORMATION OF RE-CONFIGURABLE ASSEMBLY CELLS-A USE CASE STUDY

The current market turbulence has forced the companies to increase their productivity in order to remain in business, not only to remain competitive. Companies that make high volume products involving labour-intensive assembly operation normally use automated assembly since it may reduce the company cost and increase productivity. Improving productivity is focused in the assembly area since it contributes a bigger portion of manufacturing cos

FAILURE PROBABILITY MODELING FOR PIPING SYSTEMS SUBJECT TO CORROSION UNDER INSULATION

Corrosion under insulation (CUI) is found to be a major problem for insulated piping systems in refineries, petrochemical and gas processing plants. Since those pipes carry hydrocarbons or other dangerous process fluids, gradual thinning due to CUI may cause the pipes to leak, leading to a hazardous situation. Due to the nature of CUI which is hidden, the challenge is in the monitoring, detection and, hence, prediction of CUI. Also, due to scarcity of data, the current CUI inspection and maintenance strategy adopts the risk-based inspection (RBI) approach where the assessment of the probability of failure for CUI adopts either the qualitative or semi-quantitative methods. These approaches were highly subjective and to overcome this drawback, the quantitative approach is usually employed where this approach bases the failure probability estimates on historical failure data. This study presents a methodology for quantitatively estimating the probability of failure of piping systems subject to CUI based on the type of data available. In the absence of failure data and wall thickness data, logistic regression model was proposed by considering the inspection data as a binary data. When the wall thickness data is available, the probabilistic models, namely degradation analysis, structural reliability analysis and Markov chain model, were proposed. The study recommended that for the case where wall thickness data is minimal, a good model that can be used for quantitative risk assessment is the structural reliability analysis. If more wall thickness data is available, degradation analysis and Markov chain model are the potential models. This study also demonstrated that the logistic regression model is not applicable for quantitative risk assessment. In summary, the quantitative approach is necessary as a means for quantitatively establishing future reliability for piping systems subject to CUI. Even though applying the quantitative method is optional in the current RBI analysis, quantitative risk assessment is, in fact, now a required element of the maintenance optimization methodology

A Study on Comparison of Classification Algorithms for Pump Failure Prediction

The reliability of pumps can be compromised by faults, impacting their functionality. Detecting these faults is crucial, and many studies have utilized motor current signals for this purpose. However, as pumps are rotational equipped, vibrations also play a vital role in fault identification. Rising pump failures have led to increased maintenance costs and unavailability, emphasizing the need for cost-effective and dependable machinery operation. This study addresses the imperative challenge of defect classification through the lens of predictive modeling. With a problem statement centered on achieving accurate and efficient identification of defects, this study’s objective is to evaluate the performance of five distinct algorithms: Fine Decision Tree, Medium Decision Tree, Bagged Trees (Ensemble), RUS-Boosted Trees, and Boosted Trees. Leveraging a comprehensive dataset, the study meticulously trained and tested each model, analyzing training accuracy, test accuracy, and Area Under the Curve (AUC) metrics. The results showcase the supremacy of the Fine Decision Tree (91.2% training accuracy, 74% test accuracy, AUC 0.80), the robustness of the Ensemble approach (Bagged Trees with 94.9% training accuracy, 99.9% test accuracy, and AUC 1.00), and the competitiveness of Boosted Trees (89.4% training accuracy, 72.2% test accuracy, AUC 0.79) in defect classification. Notably, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) exhibited comparatively lower performance. Our study contributes valuable insights into the efficacy of these algorithms, guiding practitioners toward optimal model selection for defect classification scenarios. This research lays a foundation for enhanced decision-making in quality control and predictive maintenance, fostering advancements in the realm of defect prediction and classification

Failure Analysis of High Pressure High Temperature Super- Heater Outlet Header Tube in Heat Recovery Steam Generator

Heat Recovery Seam Generator (HRSG) tube failure is one of the most frequent causes of power plant forced outage. In one of the local power plants, one of the boilers has experienced several defects and failures after running approximately 85,000 hours. 17 tube failures were found at the High Pressure High Temperature Superheater (HPHTSH) outlet header. The aim of this study is to find the root cause of the tube failures and to suggest the remedial action to prevent repetitive failure event. Several analysis methods were conducted to ascertain the potential cause(s) of failure. The results showed that the tubes failed due to long-term creep and thermal fatigue based on the cracking behaviour. Furthermore, the power plant has been operating as a peaking plant which concluded that the tubes have undergone the thermal stress due to frequent temperature change in the tubes. Flow correcting device (FCD) was also found damaged, causing flow imbalance in the tubes. Flow imbalance accelerated the creep degradation on the tubes. It was recommended that the FCD has to be repaired and improved to balance the flow. Furthermore, the extensive life assessment was recommended to be done on all the tubes to avoid future tube failures

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

Application of filament winding technology in composite pressure vessels and challenges : A review

The filament winding (FW) technology is one of the emerging manufacturing practices with a high degree of excellence and automation that has revolutionized gas storage and transportation doctrine. Various pressure vessels have evolved in the last few decades, from metal to fiber-reinforced tanks, primarily for weight savings and high-pressure ratings; advantageously, Type 4 composite pressure vessels (CPVs) can affect fuel gas tanks' weight savings to 75% compared to metallic vessels. As a result, composite pipelines and CPV manufacturing through FW technology have proliferated. Though many design and manufacturing challenges are associated with various process factors involved in winding technology, careful considerations are needed to create a reliable product. Therefore, it is essential to comprehend the various process parameters, their combined effects, and the associated challenges while designing and fabricating filament-wound structures. This article reviews the FW technique's utility, its evolution, various process parameters, and the CPVs as an emerging contender for high-pressure gas and cryo fluid storage. In addition, different optimization techniques, numerical analysis strategies, and challenges are summarized with related disputes and suggestions

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

Integrating Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) Model for Selection of Centralized Chilled Water Generation — Review Paper

This paper presents a review of literature relating to Life Cycle Costing (LCC) and Life Cycle Assessments (LCA) in Gas District Cooling plant. The gas district cooling plant uses either vapour compression process or absorption process for the chilled water generation. Both processes have impacts on the economic aspects as well as the contribution to CO2 emissions; thus, integration of the cost and environmental analysis of both processes are necessary. An extensive body of literature exists on both subjects, however, there is very limited number of available literature on the integration of LCC and LCA. The purpose of this review is to find the most suitable optimization model which can integrate the LCC and LCA to provide the best decision in choosing the most cost-effective and environment-friendly system. This review assesses the literature from thirty-four journals, research papers, and case reports from the year 1995 to 2017. The result of this review shows that the goal programming methodology is the appropriate method for integrating LCC and LCA because it provides the optimal decision in choosing the most cost-effective, environment-friendly system for the chilled water generation

Integrating Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) Model for Selection of Centralized Chilled Water Generation — Review Paper

This paper presents a review of literature relating to Life Cycle Costing (LCC) and Life Cycle Assessments (LCA) in Gas District Cooling plant. The gas district cooling plant uses either vapour compression process or absorption process for the chilled water generation. Both processes have impacts on the economic aspects as well as the contribution to CO2 emissions; thus, integration of the cost and environmental analysis of both processes are necessary. An extensive body of literature exists on both subjects, however, there is very limited number of available literature on the integration of LCC and LCA. The purpose of this review is to find the most suitable optimization model which can integrate the LCC and LCA to provide the best decision in choosing the most cost-effective and environment-friendly system. This review assesses the literature from thirty-four journals, research papers, and case reports from the year 1995 to 2017. The result of this review shows that the goal programming methodology is the appropriate method for integrating LCC and LCA because it provides the optimal decision in choosing the most cost-effective, environment-friendly system for the chilled water generation

Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review

A system’s operational life cycle now includes an integrated health management and diagnostic strategy due to improvements in the current technology. It is evident that the life cycle may be used to identify abnormalities, analyze failures, and forecast future conditions based on current data. Data models can be trained using machine learning and statistical ideas, employing condition data and on-site feedback. Once data models are trained, the data-processing logic can be integrated into onboard controllers, allowing for real-time health evaluation and analysis. Interestingly, the oil and gas industries may encounter numerous obstacles and hurdles as a result of the integration, highlighting the need for creative solutions to the perplexing problem. The potential benefits in terms of challenges involving feature extraction and data classification, machine learning has received significant research attention recently. The application and utility in pump system health management should be investigated to explore the extend it can be used to increase overall system resilience or identify potential financial advantages for maintenance, repair, and overhaul activities. This is seen as an evolving research area, with a variety of application domains. This article present a critical analysis of machine learning’s most current advances in the field of artificial intelligence-based system health management, specifically in terms of pump applications in the oil and gas industries. To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. There are, few drawbacks that is seen to be preventing its widespread adoption which prompt for further improvement. The article discussed possible solutions to the identified drawbacks and future opportunities presented. This study further elaborates on the commonly available commercial machine learning (ML) tools used for pump fault prognostics and diagnostics with an emphasis on the type of data utilized. Findings from the literature review shows that the neural network (NN) is the most prevalent algorithm employed in studies, followed by the Bayesian network (BN), support vector machine (SVM), and hybrid models. While the need for selecting appropriate training algorithms is seen to be significant. Interestingly, no specific method or algorithm exists for a given problem instead the solution relies on the type of data and the algorithm’s or method’s aptitude for resolving the provided errors. Among the various research studies on pump fault diagnosis and prognosis, the most frequently discussed problem is a bearing fault, with a percentage of 46%, followed by cavitation. The studies rank seal damage as the third most prevalent flaw. Leakage and obstruction are the least studied defects in research. The main data types used in machine learning techniques for diagnosing pump faults are vibration and flow, which might not be sufficient to identify the condition of pumps and their characteristics. The various datasets have been derived from expert opinion, real-world observations, laboratory tests, and computer simulations. Field data have frequently been used to create experimental datasets and simulated data. In comparison to the algorithmic approach, the data approach has not received significant research attention