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 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.

Hydrogen sulfide (${\mathrm{H}}_2$S) and carbon dioxide (${\mathrm{CO}}_2$) can cause various damages that degrade the quality of natural gas before it reaches end users and contribute to environmental pollution. Therefore, it is crucial to eliminate these contaminants to ensure effective usage and prolong equipment lifetime. An acid gas removal unit (AGRU) is a well-known type of equipment for removing ${\mathrm{H}}_2$S and ${\mathrm{CO}}_2$ from natural gas. Forty-six recent research papers have focused on the landscape of the AGRU process and its optimization strategies through experimental and simulation methods, which rely heavily on mathematical approaches. These techniques are often costly and time-consuming. Conversely, discussions on data-driven approaches as optimization techniques for AGRUs are limited. Therefore, this review highlights the potential advancements of data-driven strategies toward AGRU performance. Numerous predictive models of AGRU-related parameters, including ${\mathrm{H}}_2$S and ${\mathrm{CO}}_2$, operating parameters, and material discovery, are discussed in detail. Furthermore, predictive models of fault detection and its prevention are also examined. The literature confirms that data-driven approaches exhibit effective capabilities to enhance the operational performance of AGRUs. This could help industry operators and stakeholders maintain reliable operation and optimize the AGRU’s performance.

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 (NO_x) 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.

With the escalating demand for Radio Frequency Identification (RFID) technology and the Internet of Things (IoT), there is a growing need for sustainable and autonomous power solutions to energize low-powered devices. Consequently, there is a critical imperative to mitigate dependency on batteries during passive operation. This paper proposes the conceptual framework of rectenna architecture-based radio frequency energy harvesters’ performance, specifically optimized for low-power device applications. The proposed prototype utilizes the surroundings’ Wi-Fi signals within the 2.4 GHz frequency band. The design integrates a seven-stage Cockroft-Walton rectifier featuring a Schottky diode HSMS286C and MA4E2054B1-1146T, a low-pass filter, and a fractal antenna. Preliminary simulations conducted using Advanced Design System (ADS) reveal that a voltage of 3.53 V can be harvested by employing a 1.57 mm thickness Rogers 5880 printed circuit board (PCB) substrate with an MA4E2054B1-1146T rectifier prototype, given a minimum power input of −10 dBm (0.1 mW). Integrating the fabricated rectifier and fractal antenna successfully yields a 1.5 V DC output from Wi-Fi signals, demonstrable by illuminating a red LED. These findings underscore the viability of deploying a fractal antenna-based radio frequency (RF) harvester for empowering small electronic devices.

The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (H₂S) and carbon dioxide (CO₂), from natural gas before delivery to end-users. Consequently, chemical absorption-based acid gas removal units (AGRUs) are widely implemented due to their high efficiency and reliability. The most common solvent used in AGRU is monodiethanolamine (MDEA), often mixed with piperazine (PZ) as an additive to accelerate acid gas capture. The absorption performance, however, is significantly influenced by the solvent mixture composition. Despite this, solvent composition is often determined through trial and error in experiments or simulations, with limited studies focusing on predictive methods for optimizing solvent mixtures. Therefore, this paper aims to develop a predictive technique for determining optimal solvent compositions under varying sour gas conditions. An ensemble algorithm, Extreme Gradient Boosting (XGBoost), is selected to develop two predictive models. The first model predicts H₂S and CO₂ concentrations, while the second model predicts the MDEA and PZ compositions. The results demonstrate that XGBoost outperforms other algorithms in both models. It achieves R² values above 0.99 in most scenarios, and the lowest RMSE and MAE values of less than 1, indicating robust and consistent predictions. The predicted acid gas concentrations and solvent compositions were further analyzed to study the effects of solvent composition on acid gas absorption across different scenarios. The proposed models offer valuable insights for optimizing solvent compositions to enhance AGRU performance in industrial applications.