Compressed air systems are widely used in industry, and air leaks that occur over time lead to significant and unnecessary energy losses. This study aims to quantify the energy-saving potential of compressed air leaks in a manufacturing plant and to develop machine learning (ML) regression models for sustainable leak management. A total of 230 leak points were identified by measuring three periods using an ultrasonic device. Using the measured acoustic emission level (dB) and probe distance (x) as inputs, the leak flow rate, annual energy-saving potential, cost loss, and carbon footprint were calculated. As a result of the repairs, energy consumption improved by 8% compared to the initial state. Three regression models were compared to predict leak flow: Linear Regression, Bagging Regression Trees, and Multivariate Adaptive Regression Splines. Among the models evaluated, the Bagging Regression Trees model demonstrated the best prediction performance, achieving an R² value of 0.846, a mean squared error (MSE) of 389.85 (L/min²), and a mean absolute error (MAE) of 12.13 L/min in the independent test set. Compared to previous regression-based approaches, the proposed ML method contributes to sustainable production strategies by linking leakage prediction to energy performance indicators.

Friction stir welding (FSW) is the most widely used solid-state joining method for sheet and plate-like materials, valued for being a highly adaptable, environmentally sound, and energy-efficient process. The literature has repeatedly shown that FSW is a reliable joining technique for high-demand technological applications, particularly for high-strength aluminum and titanium alloys used in aerospace, which are difficult to join with traditional fusion welding methods. To describe the microstructural changes caused by solid-state FSW, many studies analyze mechanical parameters of FSW joints, including tensile strength, bending, torsion, elasticity, and fatigue responses. In recent years, the push to expand FSW's use, broaden the range of compatible alloy systems, and improve the resulting mechanical properties has led to significant advancements in this joining method. Accordingly, this review study provides a comprehensive investigation into the methodology and the materials relevant to this specific field.

Jet impingement is a widely utilized technique in engineering, particularly for cooling high-temperature systems like aircraft engines and electronic devices. This study employs numerical analysis to examine the flow dynamics of dual impinging pulsating nanofluid jets, utilizing the ANSYS software platform. This research examines the combined impact of key parameters, including jet geometry, pulsation frequency and amplitude, nanoparticle volume concentration, and Reynolds numbers, on the efficiency of heat transfer. The impact of aluminum oxide (Al₂O₃) nanofluids with varying concentrations (1%, 2%, 4%, and 5%) on thermal performance is assessed. The findings of the study demonstrate that the pulsating jets generate bidirectional swirling flows and reverse vortices upon impact with the surface, resulting in notable enhancements in local heat transfer rates. These vortices expand and form wall jets, which contribute to an increase in the heat transfer coefficients and Nusselt numbers. The simulations demonstrate that higher pulsation frequencies (30 Hz) result in a 10% increase in heat transfer efficiency compared to lower frequencies (10 Hz). This is attributed to enhanced flow dynamics and improved heat distribution. Moreover, the incorporation of nanoparticles markedly enhances heat transfer efficiency. The Nusselt numbers were observed to increase by 18% when the concentration of nanoparticles reached 5%, in comparison to plain water. Additionally, the study underscores the significance of jet spacing, wherein an optimal separation distance of 100 mm between the dual jets was identified as a means of maximizing heat transfer by fostering effective vortex interactions. Higher Reynolds numbers contribute to the formation of thinner thermal boundary layers, thereby facilitating increased heat transfer rates, particularly at the stagnation points where the flow impinges directly on the surface. Overall, the study demonstrates that substantial enhancements in heat transfer can be achieved by optimizing key parameters such as pulsating frequency, amplitude, nanoparticle volume concentration, and jet distances.

This study presents the results of a comprehensive energy audit conducted at an industrial textile dyeing facility in Türkiye with the aim of evaluating its energy performance and identifying energy efficiency improvement potentials. Electricity and natural gas consumption patterns were analyzed using historical production and energy data, field measurements, and on-site observations. The audit focused particularly on thermal energy systems, including steam boiler operation, heat distribution lines, and auxiliary equipment, as well as electrical systems such as lighting. Flue gas analysis, thermal imaging, and energy monitoring were employed to determine system efficiencies and quantify energy losses. Based on the audit findings, three main energy efficiency measures were proposed: recovery of waste heat from boiler flue gases, insulation of uninsulated piping and installation elements, and conversion of conventional lighting systems to high-efficiency LED luminaires. The results indicate that natural gas accounts for more than 90% of total energy consumption, highlighting the critical importance of improving thermal systems. The proposed measures demonstrate significant energy saving and economic benefits, with simple payback periods ranging between 0.6 and 3.9 years. The findings confirm that systematic energy audits constitute an effective and economically feasible approach for reducing energy consumption, operating costs, and greenhouse gas emissions in energy-intensive textile dyeing facilities.