Articles in This Issue
Abstract
Response Surface Methodology (RSM) has been widely applied in drilling engineering to model and optimize multivariable operational systems. This review critically evaluates recent RSM applications in drilling engineering, identifies their methodological limitations, and examines the extent to which bit-level hydraulic performance parameters have been incorporated into drilling optimization frameworks. A structured review of studies published between 2021 and 2026 was conducted. The selected studies were classified into three categories: mechanical performance optimization, drilling-fluid optimization, and hybrid optimization approaches. Each study was evaluated according to its input variables, response variables, modeling techniques, optimization objectives, reported performance improvements, consideration of physical and operational constraints, and integration of data-driven methods such as Artificial Neural Networks (ANN). The review indicates that most RSM-based studies prioritize outcome-based indicators, particularly rate of penetration (ROP), drilling-fluid rheology, and operational parameter selection. In contrast, bit-level hydraulic parameters, specifically Bit Hydraulic Horsepower (BHHP) and Jet Impact Force (JIF), are rarely treated as primary optimization objectives despite their direct influence on hydraulic energy transfer, rock fragmentation, and cuttings removal at the bit–rock interface. The findings also show that most existing studies employ single-objective optimization and second-order polynomial models, which may limit their ability to represent nonlinear and coupled hydraulic behavior under field operating conditions. This review adds to the existing literature by systematically identifying the limited integration of physics-based hydraulic indicators into RSM-driven drilling optimization and by defining a research direction for hybrid frameworks that combine RSM-based design-space validation, ANN-based nonlinear prediction, and constrained multi-objective optimization of BHHP and JIF. Such frameworks could support more robust, scalable, and field-applicable drilling optimization strategies.
Abstract
In this paper, a new correlation was developed (139 data sets) in this study to predict the oil recovery for sandstone solution gas drive reservoirs (SGDR) using NLMR with a coefficient of determination, R2 of 0.90 (compared to 0.87 for Gulstad correlation). An artificial neural network (ANN) model was developed for this reservoir giving an R2 of 0.92. A correlation was developed (111 data sets) in this study to predict the oil recovery for sandstone water drive reservoirs (SWDR) using NLMR with a coefficient of determination, R2 of 0.93 (compared to 0.91 for American Petroleum Institute, API correlation). The developed ANN model for this reservoir gives an R2 of 0.94.
Abstract
The worldwide evolution towards cleaner and more sustainable energy infrastructures has fostered intense research on Hybrid Renewable Energy Systems (HRES) that combine two or more energy sources that are complementary to each other to tackle the limitations of individual renewables. This paper reviews the optimization, control, and energy management strategies used for the HRES and provides a critical short review of the same. This paper covers system classification, component modeling, classical and metaheuristic optimization techniques, AI-based energy management, and techno-economic assessment. The review also highlights key current challenges and offers directions for future research, including AI-based smart systems, hydrogen integration, digital twins, and blockchain-based energy trading.
Abstract
Autism spectrum disorder (ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in individuals. Global health organization facing difficulties in establishing an effective ASD diagnostic system that facilitates precise analysis and early autism prediction. It is a scientific issue that necessitates resolution. This research presents an approach for the early prediction of children with ASD utilizing significant variables through machine learning (ML) methods. Three stages comprise the suggested technique. First, a 1250-case ASD dataset was identified and preprocessed. Five extremely effective traits with high Pearson correlation coefficient (PCC) are chosen from 10 chosen ASD feature dataset through its paces using five ML techniques: Naive Bayes (NB), K-Nearest Neighbor (kNN), Decision Tree(DT), Support Vector Machine (SVM), and AdaBoostM1 (ABM1). The proposed framework is assessed in the third phase utilizing five measurements such as accuracy, precision, predicting time ,recall, and F1-score, . The findings revealed that: NB and K-NN approaches exhibit superior accuracy rates of 99.2% and 97.2%, with minimal prediction times of approximately 0.3 and 0.45 seconds, correspondingly. Conversely, the DT and AdBM1 methods demonstrate a minor decline in accuracy, achieving 94.8% and 87.6%, respectively, along with increased prediction times. Nonetheless, the SVM approach exhibits the least performance, achieving an accuracy of 80.4% with prediction time of 0.84 seconds. This research aligns with several Sustainable Development Goals (SDGs), including ٍSDG3, which focuses on good health, SCG10, which addresses reducing inequalities, including those related to disability. and SCG9 by strengthening technological capabilities, particularly in scientific research
Abstract
Shadows dramatically reduce solar panel output because individual cells are wired in chains (series). Blocking one cell chokes the current for the entire chain. While bypass diodes bypass shaded sections, severe shadows can still cause "hot spots", which permanently damage panels. The proposed work presents an experimental measurements on the impact of shading on monocrystalline power losses. The solar module is exposed under the real environment conditions for direct solar radiation of 500 W/m2 and 80 W/m2 solar radiation at penumbra shading of shaded area with operating solar module temperature 25oC at ambient temperature 18oC. The Solar Module Analyzer simulator was used in this study, where 10 pattern of shading are tested. The photovoltaic solar module performance in terms of outlet power and fill factor with its corresponding efficiency are greatly affected by shadow. The maximum output power drops drastically, often much more than the shaded area percentage would suggest ; 25% shading can lead to 60% power loss and also 50% shading can lead to 60% power loss. It is noticeable that there is a sudden change in the behavior of the I-V and P-V characteristic curves at a certain range of open circuit voltage 9-9.5V.
Abstract
Networked Control Systems (NCSs) are increasingly deployed in critical infrastructure, yet their performance is often compromised by communication impairments such as time-varying delays and packet loss. These disturbances can undermine stability, reliability, and overall efficiency. To address these challenges, this study proposes an AI-augmented supervisory control framework that integrates communication metrics directly into the control strategy rather than relying solely on conventional error signals. The architecture employs machine learning classifiers—specifically K-Nearest Neighbors (KNN) and Random Forest (RF)—to assess real-time network conditions (normal, degraded, and emergency) and dynamically adjust the parameters of a core PID controller. Under degraded conditions, PID gains are attenuated to enhance robustness against latency, while in emergency scenarios, a conservative safe-mode configuration is activated to preserve plant stability under severe packet loss. Comparative analysis demonstrates that Random Forest, due to its ensemble-based design, achieves superior situational awareness, smoother mode transitions, and more reliable global stability than KNN. Moreover, RF reduces volatile parameter updates, thereby protecting physical actuators from excessive stress and extending component lifespans. The findings highlight the potential of ensemble learning approaches to enhance resilience and operational safety in delay-prone NCS environments.