Rapid detection of hazardous chemical leaks in industrial facilities using a UAV–IMS system: Optimization of flight parameters for emergency response

Consequence analysis of a small-scale hydrogen leakage from the overhead hydrogen piping based on machine learning and physical modeling

The sensing of ambient gases is of growing interest for many applications as for example indoor air quality monitoring, gas leakage or fire detection. We present the fabrication of high aspect-ratio silicon (Si) pillar-based hybrid plasmonic-photonic crystal waveguides. This type of waveguide is suitable for ultra-sensitive gas sensing applications by providing enhanced light-matter interaction due to low group velocities (slow light) in photonic crystal waveguides and high evanescent field ratio. Eight-inch silicon substrate wafers are processed by means of lithography and deep reactive ion etch (DRIE, aka. Bosch etch), which took place in the industrial clean-room facilities of Infineon Technologies Austria AG in Villach. The fabrication processes were optimized to a level such that aspect ratios of around seven for pillars of 510 nm diameter could be reached. The discrepancy of measured diameters and diameters given by the design was found to be lower than 50 nm, with a standard deviation for the measured diameters lower than 20 nm.

PurposeThis paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.Design/methodology/approachThe optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.FindingsThe optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.Research limitations/implicationsIn this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.Practical implicationsThe study presents the case of an automotive component, which illustrates practical relevance.Originality/valueIn several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

Copper alloys have important industrial roles due to their excellent properties. Beryllium copper is one of the copper-based alloys with the highest strength and excellent metal working and machining properties. Beryllium copper alloy composite is fabricated by the stir casting method. Its characteristics are studied with scanning electron microscopy and energy dispersive analysis of X-rays. The material properties of the stir cast beryllium copper alloy composites are evaluated. Hardness can be increased by the addition of silicon carbide particles. Abrasive water jet machining is an effective unconventional machining process, in which high pressure water jet and abrasive particles are used to remove material. Response parameters such as material removal rate and circularity of holes are considered. Response parameters of abrasive water jet machining are compared to electrical discharge machining. The most influential factor for the responses is determined by analysis of variance. Optimization of machining parameters is performed with the Taguchi method.

Gas metal arc welding is one of the arc fusion processes that is widely used in industry due to its high efficiency. The correct selection of the input parameters has direct influence on the weld quality and, with the control of those parameters, it is possible to reduce the amount of weld material, improve its properties and then increase the productivity of the process. This study intends to take a group of weld parameters and submit them to the optimization by the Taguchi Method and check the influence of those through a Variance Analysis (ANOVA). An L9 orthogonal array gathered three parameters (weld voltage, weld speed and weld torch angle) into three levels, then, with all combinations set and performed, the macrography and the transversal tensile strength test provided, respectively, the geometrical and the mechanical properties. The signal-to-noise ratios enable the optimization and the ANOVA provided the influence of the input parameters on the response parameters. The weld speed appeared as the most influent parameter for the weld geometry, contributing 63.54% to reinforcement, 66.36% to width and 66.94% to penetration, and the weld torch angle the most influent to the ultimate transversal tensile strength (41.39%). The optimum levels to the reinforcement are 22.4 [V], 400 [mm/min] and 30 [°], to the width 22.4 [V], 300 [mm/min], 0 [°], to the penetration 23.3 [V], 400 [mm/min], 0 [°] and, lastly, to the ultimate transversal tensile strength 24.1 [V], 200 [mm/min], 15 [°]. The Taguchi method showed to be suitable for this kind of problem and giving an efficient experiment design and good results.
 Keywords: Taguchi method, Optimization, GMAW

Optimization of process parameters based on RSM and GRA method for machining of Inconel-600 by electric discharge machining

PurposeThe purpose of this paper is to present an optimisation of four-point star-shaped structures produced through additive manufacturing (AM) polylactic acid (PLA). The study also aims to investigate the compression failure mechanism of the structure.Design/methodology/approachA Taguchi L9 orthogonal array design of the experiment is adopted in which the input parameters are resolution (0.06, 0.15 and 0.30 mm), print speed (60, 70 and 80 mm/s) and bed temperature (55°C, 60°C, 65°C). The response parameters considered were printing time, material usage, compression yield strength, compression modulus and dimensional stability. Empirical observations during compression tests were used to evaluate the load–response mechanism of the structures.FindingsThe printing resolution is the most significant input parameter. Material length is not influenced by the printing speed and bed temperature. The compression stress–strain curve exhibits elastic, plateau and densification regions. All the samples exhibit negative Poisson’s ratio values within the elastic and plateau regions. At the beginning of densification, the Poisson’s ratios change to positive values. The metamaterial printed at a resolution of 0.3 mm, 80 mm/s and 60°C exhibits the best mechanical properties (yield strength and modulus of 2.02 and 58.87 MPa, respectively). The failure of the structure occurs through bending and torsion of the unit cells.Practical implicationsThe optimisation study is significant for decision-making during the 3D printing and the empirical failure model shall complement the existing techniques for the mechanical analysis of the metamaterials.Originality/valueTo the best of the authors’ knowledge, for the first time, a new empirical model, based on the uniaxial load response and “static truss concept”, for failure mechanisms of the unit cell is presented.

The article addresses the current challenges of developing a digital methodology for managing organizational and technological processes in construction, aimed at optimizing project parameters and enhancing the efficiency of investment and construction programs. In the context of increasing project complexity, strict time constraints, and the need for rational resource utilization, digital transformation becomes a decisive factor for ensuring the competitiveness of construction enterprises.The study investigates the key directions of applying digital technologies in construction, including Building Information Modeling (BIM), tools of scheduling and network planning, digital platforms for risk and resource management, and integrated project monitoring systems. It is demonstrated that the combination of methodological approaches with modern software solutions creates the foundation for flexible management of organizational and technological decisions. Such integration allows rapid adaptation to external and internal changes and minimizes the negative impact of uncertainty on project performance.Particular attention is paid to the optimization of construction duration and cost through the integration of digital tools into management processes. The research substantiates that the digital management methodology enables multifactor analysis, scenario forecasting, and the formation of adaptive organizational and technological solutions. The effectiveness of these solutions is reflected in higher labor productivity, reduced costs, minimized risks of schedule disruptions, and improved quality of construction and installation works.The paper also discusses practical aspects of applying digital methods in Ukrainian construction companies engaged in reconstruction, recovery, and the erection of new industrial and civil facilities. Emphasis is placed on the role of digitalization in coordinating stakeholder interactions, ensuring transparency in decision-making, and creating a unified information environment for managing data throughout the entire life cycle of a construction project.The findings have both theoretical and practical significance. They contribute to the further development of methodological foundations for construction organization with the use of digital technologies, as well as the formation of innovative mechanisms for project parameter optimization, ensuring high levels of efficiency and sustainability in construction projects.

Chemical sensors are used in various industrial facilities such high-risk and prevent the leakage of substances, important in life and environmental protection and the safe use of industry, used for management. In particular, high-temperature environments such as power generation equipment of the rotating part due to leakage generated by the various oil, power plants Shut Down, fire, work environment (exposure to various chemical solution and gas leak) and various water, air and soil pollution causes. Thus, over the long term through various channels such as crops and groundwater contamination caused by the slow, serious adverse effect on the ecosystem. In this paper, synthetic lube oil and high pressure hydraulic fluid leakage and immediately detect a new Printed Electronic implementation of technology-based film-type sensors, and its performance test. Thus, industrial accidents and environmental pollution and for early detection of problems, large accidents can be prevented. Experimental results of the synthetic lube oil and high pressure hydraulic fluid solution after the contact time depending on the experiment and the oil solution of the sensor material of the conductive porous PE resistance value by a chemical reaction could be confirmed that rapid increase. Also implemented in the film-type oil sensor electrical resistance change over time of the reaction rate and the synthetic lube oil is about 2 minutes or less, the high pressure hydraulic fluid is less than about 1 minute was. Therefore, more high-pressure hydraulic fluid such as a low volatility synthetic lube oils are the resistance change and the reaction rate was confirmed to be the slowest.

BackgroundFluorescence quenching is an interesting phenomenon with the potential to be applied across various fields. The mechanism is commonly used across analytical applications for monitoring the concentration of trace substances. Naphthalimide and its family of compounds are commonly used as fluorescent detectors. This work investigated an analytical technique through which naphthalimide-based dyes could be quantified. A commercial A/C leak detector was used as the dye and Cu2+ ions as the quencher. Experiments were also conducted to investigate the effect of temperature on quenching. To study the mechanism of quenching further, density functional theory (DFT) was used.ResultsThe method detection limit obtained in this work is 1.7 × 10–6 mol/L. The results from the quenching experiments demonstrated a pattern which fit a modified Stern–Volmer (SV) model, with an R2 value of 0.9886. From the experiments on the effect of temperature, a dynamic quenching behavior was observed given the emission spectra demonstrated an inverse relationship with temperature.ConclusionsThe quenching of the commercial A/C dye by Cu2+ ions can be used to develop a rapid and sensitive detection method for metal ions such as Cu2+, and for future fabrication of chemosensors for Cu2+.

Objective: This study aimed to implement a model for detecting and locating leaks in water distribution networks, using artificial intelligence (artificial neural networks). Background: Traditional leak detection methods, such as visual inspections and flow and pressure analysis, are being progressively complemented by solutions based on smart sensors and machine learning techniques. Method: Using the method of characteristics to solve the momentum and continuity equations, along with nodal equations, a hydraulic transient simulator was created for water distribution networks with leaks. For the leak simulation, the orifice law (Torricelli's Law) was used. Results and Discussion: The developed model successfully located the leak with an error of approximately 5.65%, corresponding to a distance of approximately 61.22 m for the studied network. Research Implications: Deteriorated infrastructure in water distribution systems can lead to leakage losses, reduced water transport capacity, component failures, increased maintenance and operating costs, frequent system interruptions, and decreased reliability. Thus, the exploration of rapid leak detection and location methods has increased, aiming to mitigate environmental impacts and the losses of natural and financial resources. Originality/Value: The methodological distinction lies in using pressure wave travel times as input data, rather than absolute pressure values. This approach eliminates the need for detailed hydraulic network calibration, reduces sensitivity to operational noise, and broadens the practical applicability of the model.

Valve integrity is a cornerstone of process safety, operational efficiency, and cost-effective management in industries such as oil and gas, petrochemicals, and power generation. Ensuring that valves function correctly is crucial because their failure or leakage can lead to severe consequences, including operational inefficiencies, environmental hazards, and safety risks. Traditional valve inspection techniques, including manual inspections, ultrasonic testing, and pressure-based methods, are often labor-intensive, time-consuming, and require specialized training. These methods can also disrupt operations, leading to costly downtime and reduced productivity. Manual inspections rely heavily on the experience and expertise of the operator, making them prone to human error and subjectivity. Ultrasonic testing, while accurate, requires specialized equipment and trained personnel, adding to the complexity and cost of the inspection process. Pressure-based methods, such as pressure drop tests, are effective in detecting leaks but necessitate process shutdowns, causing operational disruptions and significant downtime. These challenges highlight the need for more efficient, accurate, and non-intrusive inspection solutions. This paper explores and implements a smart valve inspection solution that leverages acoustic emission (AE) technology and I-safe smartphone devices for real-time leak detection. AE technology captures sound waves generated by turbulent flow in leaking valves, offering a non-intrusive, highly accurate, and efficient method of inspection. The use of I-safe smartphones, which are certified for use in hazardous areas, allows for safe and convenient data collection and processing in real-time. A field implementation was conducted at two industrial facilities, referred to as Location A and Location B, where valves were tested under varying operational conditions. The system demonstrated high accuracy in detecting leaks, significantly reduced inspection times, and provided substantial cost savings compared to traditional methods. Data collected by the AE sensors is processed automatically, stored securely in the cloud, and does not require operator expertise, making the system user-friendly and accessible. A cost analysis revealed a substantial reduction in recurring testing costs, with a break-even period of less than six months. The smart valve inspection system's ability to provide real-time, non-intrusive monitoring without the need for specialized training or equipment makes it a highly attractive alternative to conventional methods. This paper compares AE-based valve inspection with traditional techniques, highlighting its benefits in terms of operational efficiency, safety, and scalability. The study contributes to the digital transformation of industrial maintenance by demonstrating the practical application of AE technology and I-safe devices in valve inspection. It also proposes further advancements through the integration of artificial intelligence (AI) and the Internet of Things (IoT) to enhance the system's capabilities. These advancements could lead to predictive maintenance solutions that further improve operational efficiency and safety in industrial settings.

The rapid advancement of the Unmanned Aerial Systems (UAS) technology and applications provide a unique opportunity to assist the United Nations with their 17 Sustainable Development Goals (SDG’s). Further enhancement of the UAS sector has brought forward tangible applications that illustrate how this technology can assist in improving community health, collective education and stimulate economic growth. There are numerous practical examples of how UAS contribute to the SDG’s of climatologists today. Outlined in this paper are some of the most prominent UAS applications and their resulting benefits to society. The development and global roll out of the UAS has created significant opportunity for climate scientists studying toxic gas emissions and the chemistry of Earth’s atmosphere. UAS make emission monitoring more accurate, contribute to gas leak detection at industrial facilities and greatly assist plant efficiency and optimization. UAS help simplify the data collection process by allowing remote pilots to quickly survey industrial plants and allow them to carry payloads of scientific equipment such as spectrometers which can be used for Differential Optical Absorbance Spectroscopy (DOAS) or thermal cameras designed to detect thermal anomalies. The research illustrated here shows real-world practical examples of using UAS technology on natural emissions at the active crater of the Poás Volcano in Costa Rica and the Industrial power plant of Tampa Electric (TECO) in Tampa, Florida.

This article discusses the critical importance of adhering to occupational safety and environmental standards in the chemical industry, particularly when working with hazardous substances. Chemical production involves the handling of toxic, flammable, explosive, and corrosive materials, which pose serious risks to human health and the environment if not managed properly. Therefore, ensuring workplace safety and minimizing environmental impact are essential components of sustainable chemical manufacturing. In chemical enterprises, the potential hazards include chemical spills, gas leaks, fires, explosions, and long-term exposure to harmful substances. These risks require robust safety protocols, strict regulatory compliance, and the continuous monitoring of operational conditions. The article emphasizes the need for comprehensive industrial safety management systems that cover hazard identification, risk assessment, incident prevention, and emergency response. Key aspects of occupational safety in the chemical sector include: Use of Personal Protective Equipment (PPE): Ensuring workers are equipped with and trained to use chemical-resistant gloves, goggles, respirators, protective suits, and other PPE appropriate to the substances handled. Workplace Monitoring: Regular control of air quality, temperature, pressure levels, and detection of gas leaks using automated sensors and real-time monitoring systems. Safety Training and Procedures: Periodic training on handling hazardous chemicals, proper storage and labeling, emergency evacuation, and first aid. Technical Safety Measures: Use of explosion-proof equipment, automatic shutdown systems, leak containment systems, and proper ventilation in processing areas. From an environmental perspective, the chemical industry faces challenges such as the generation of toxic waste, air and water pollution, and the consumption of large quantities of energy and raw materials.

We present a tool for modeling the performance of methane leak detection and repair programs that can be used to evaluate the effectiveness of detection technologies and proposed mitigation policies. The tool uses a two-state Markov model to simulate the evolution of methane leakage from an artificial natural gas field. Leaks are created stochastically, drawing from the current understanding of the frequency and size distributions at production facilities. Various leak detection and repair programs can be simulated to determine the rate at which each would identify and repair leaks. Integrating the methane leakage over time enables a meaningful comparison between technologies, using both economic and environmental metrics. We simulate four existing or proposed detection technologies: flame ionization detection, manual infrared camera, automated infrared drone, and distributed detectors. Comparing these four technologies, we found that over 80% of simulated leakage could be mitigated with a positive net present value, although the maximum benefit is realized by selectively targeting larger leaks. Our results show that low-cost leak detection programs can rely on high-cost technology, as long as it is applied in a way that allows for rapid detection of large leaks. Any strategy to reduce leakage should require a careful consideration of the differences between low-cost technologies and low-cost programs.