Model Predictive Control Design and Implementation on a 3x3 Model of Shell Heavy Oil Fractionator

Model Predictive Control (MPC) is the most famous advanced process control method in the industry. MPC refers to a class of computer control algorithms that utilize and explicit process model to predict the future response of the plant. Therefore, we can clearly see that this control strategy has brought a great importance for the industry to control the throughput to meet the requirement. For this purpose, a chemical process model is examined for set point tracking to measure its performance. Different direction of set point is tested for a given model, to measure optimum control horizon for the model and to study whether model is behaved efficiently for MIMO system. This study stated that given model is behaved efficiently for SISO system compared to MIMO system. This may due to modeling error in process gai

Model Predictive Control Design and Implementation on a 3x3 Model of Shell Heavy Oil Fractionator

Model Predictive Control (MPC) is the most famous advanced process control method in the industry. MPC refers to a class of computer control algorithms that utilize and explicit process model to predict the future response of the plant. Therefore, we can clearly see that this control strategy has brought a great importance for the industry to control the throughput to meet the requirement. For this purpose, a chemical process model is examined for set point tracking to measure its performance. Different direction of set point is tested for a given model, to measure optimum control horizon for the model and to study whether model is behaved efficiently for MIMO system. This study stated that given model is behaved efficiently for SISO system compared to MIMO system. This may due to modeling error in process gai

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

This paper comprehensively reviews the spiral dynamics optimization (SDO) algorithm and investigates its characteristics. SDO algorithm is one of the most straightforward physics-based optimization algorithms and is successfully applied in various broad fields. This paper describes the recent advances of the SDO algorithm, including its adaptive, improved, and hybrid approaches. The growth of the SDO algorithm and its application in various areas, theoretical analysis, and comparison with its preceding and other algorithms are also described in detail. A detailed description of different spiral paths, their characteristics, and the application of these spiral approaches in developing and improving other optimization algorithms are comprehensively presented. The review concludes the current works on the SDO algorithm, highlighting its shortcomings and suggesting possible future research perspectives

LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN BELIEF NETWORK

A Dry-Low Emission (DLE) gas turbine reduces Carbon Oxide (COx) and Nitrogen Oxide (NOx) emission during power generation. However, DLE gas turbines frequently encounter trips due to Lean Blowout (LBO) fault. The state-of-the-art studies on LBO are performed in a laboratory-scale where gas turbine dynamics are not well represented. There is a potential of utilizing a dynamic model where DLE gas turbine model is developed to predict LBO fault. However, the superior prediction technique such as Support Vector Machine (SVM) is deterministic without the probability of the impending trip. Therefore, this thesis proposes a DLE gas turbine model with a hybrid of Support Vector Machine-Bayesian Belief Network (SVM-BBN) for LBO fault prediction

Application of Neural Network in Predicting H₂S from an Acid Gas Removal Unit (AGRU) with Different Compositions of Solvents

The gas sweetening process removes hydrogen sulfide (H2S) in an acid gas removal unit (AGRU) to meet the gas sales’ specification, known as sweet gas. Monitoring the concentration of H2S in sweet gas is crucial to avoid operational and environmental issues. This study shows the capability of artificial neural networks (ANN) to predict the concentration of H2S in sweet gas. The concentration of N-methyldiethanolamine (MDEA) and Piperazine (PZ), temperature and pressure as inputs, and the concentration of H2S in sweet gas as outputs have been used to create the ANN network. Two distinct backpropagation techniques with various transfer functions and numbers of neurons were used to train the ANN models. Multiple linear regression (MLR) was used to compare the outcomes of the ANN models. The models’ performance was assessed using the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings demonstrate that ANN trained by the Levenberg–Marquardt technique, equipped with a logistic sigmoid (logsig) transfer function with three neurons achieved the highest R2 (0.966) and the lowest MAE (0.066) and RMSE (0.122) values. The findings suggested that ANN can be a reliable and accurate prediction method in predicting the concentration of H2S in sweet gas

Prediction of Dry-Low Emission Gas Turbine Operating Range from Emission Concentration Using Semi-Supervised Learning

Dry-Low Emission (DLE) technology significantly reduces the emissions from the gas turbine process by implementing the principle of lean pre-mixed combustion. The pre-mix ensures low nitrogen oxides (NOx) and carbon monoxide (CO) production by operating at a particular range using a tight control strategy. However, sudden disturbances and improper load planning may lead to frequent tripping due to frequency deviation and combustion instability. Therefore, this paper proposed a semi-supervised technique to predict the suitable operating range as a tripping prevention strategy and a guide for efficient load planning. The prediction technique is developed by hybridizing Extreme Gradient Boosting and K-Means algorithm using actual plant data. Based on the result, the proposed model can predict the combustion temperature, nitrogen oxides, and carbon monoxide concentration with an accuracy represented by R squared value of 0.9999, 0.9309, and 0.7109, which outperforms other algorithms such as decision tree, linear regression, support vector machine, and multilayer perceptron. Further, the model can identify DLE gas turbine operation regions and determine the optimum range the turbine can safely operate while maintaining lower emission production. The typical DLE gas turbine’s operating range can operate safely is found at 744.68 °C –829.64 °C. The proposed technique can be used as a preventive maintenance strategy in many applications involving tight operating range control in mitigating tripping issues. Furthermore, the findings significantly contribute to power generation fields for better control strategies to ensure the reliable operation of DLE gas turbines

The Lean Blowout Prediction Techniques in Lean Premixed Gas Turbine: An Overview

The lean blowout is the most critical issue in lean premixed gas turbine combustion. Decades of research into LBO prediction methods have yielded promising results. Predictions can be classified into five categories based on methodology: semi-empirical model, numerical simulation, hybrid, experimental, and data-driven model. First is the semi-empirical model, which is the initial model used for LBO limit prediction at the design stages. An example is Lefebvre’s LBO model that could estimate the LBO limit for eight different gas turbine combustors with a ±30% uncertainty. To further develop the prediction of the LBO limit, a second method based on numerical simulation was proposed, which provided deeper information and improved the accuracy of the LBO limit. The numerical prediction method outperformed the semi-empirical model on a specific gas turbine with ±15% uncertainty, but more testing is required on other combustors. Then, scientists proposed a hybrid method to obtain the best out of the earlier models and managed to improve the prediction to ±10% uncertainty. Later, the laboratory-scale combustors were used to study LBO phenomena further and provide more information using the flame characteristics. Because the actual gas turbine is highly complex, all previous methods suffer from simplistic representation. On the other hand, the data-driven prediction methods showed better accuracy and replica using a real dataset from a gas turbine log file. This method has demonstrated 99% accuracy in predicting LBO using artificial intelligence techniques. It could provide critical information for LBO limits prediction at the design stages. However, more research is required on data-driven methods to achieve robust prediction accuracy on various lean premixed combustors

Dry-Low Emission Gas Turbine Technology: Recent Trends and Challenges

Dry-low emission (DLE) is one of the cleanest combustion types used in a gas turbine. DLE gas turbines have become popular due to their ability to reduce emissions by operating in lean-burn operation. However, this technology leads to challenges that sometimes interrupt regular operations. Therefore, this paper extensively reviews the development of the DLE gas turbine and its challenges. Numerous online publications from various databases, including IEEE Xplore, Scopus, and Web of Science, are compiled to describe the evolution of gas turbine technology based on emissions, fuel flexibility, and drawbacks. Various gas turbine models, including physical and black box models, are further discussed in detail. Working principles, fuel staging mechanisms, and advantages of DLE gas turbines followed by common faults that lead to gas turbine tripping are specifically discussed. A detailed evaluation of lean blow-out (LBO) as the major fault is subsequently highlighted, followed by the current methods in LBO prediction. The literature confirms that the DLE gas turbine has the most profitable features against other clean combustion methods. Simulation using Rowen’s model significantly imitates the actual behavior of the DLE gas turbine that can be used to develop a control strategy to maintain combustion stability. Lastly, the data-driven LBO prediction method helps minimize the flame’s probability of a blow-out

The Lean Blowout Prediction Techniques in Lean Premixed Gas Turbine: An Overview

The lean blowout is the most critical issue in lean premixed gas turbine combustion. Decades of research into LBO prediction methods have yielded promising results. Predictions can be classified into five categories based on methodology: semi-empirical model, numerical simulation, hybrid, experimental, and data-driven model. First is the semi-empirical model, which is the initial model used for LBO limit prediction at the design stages. An example is Lefebvre’s LBO model that could estimate the LBO limit for eight different gas turbine combustors with a ±30% uncertainty. To further develop the prediction of the LBO limit, a second method based on numerical simulation was proposed, which provided deeper information and improved the accuracy of the LBO limit. The numerical prediction method outperformed the semi-empirical model on a specific gas turbine with ±15% uncertainty, but more testing is required on other combustors. Then, scientists proposed a hybrid method to obtain the best out of the earlier models and managed to improve the prediction to ±10% uncertainty. Later, the laboratory-scale combustors were used to study LBO phenomena further and provide more information using the flame characteristics. Because the actual gas turbine is highly complex, all previous methods suffer from simplistic representation. On the other hand, the data-driven prediction methods showed better accuracy and replica using a real dataset from a gas turbine log file. This method has demonstrated 99% accuracy in predicting LBO using artificial intelligence techniques. It could provide critical information for LBO limits prediction at the design stages. However, more research is required on data-driven methods to achieve robust prediction accuracy on various lean premixed combustors

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and being costly. Hence, we designed an automated model based on an artificial neural network to predict Saybolt color. The network has been built with five input variables, density, kinematic viscosity, sulfur content, cetane index, and total acid number; and one output, i.e., Saybolt color. Two backpropagation algorithms with different transfer functions and neurons number were tested. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to assess the performance of the developed model. Additionally, the results of the ANN model are compared with the multiple linear regression (MLR). The results demonstrate that the ANN with the Levenberg–Marquart algorithm, tangent sigmoid transfer function, and three neurons achieved the highest performance (R2 = 0.995, MAE = 1.000, and RMSE = 1.658) in predicting the Saybolt color. The ANN model appeared to be superior to MLR (R2 = 0.830). Hence, this shows the potential of the ANN model as an effective method with which to predict Saybolt color in real time