State-of-the-art review on the steel decarbonization technologies based on process system engineering perspective

Decarbonization of steel manufacturing requires policies to reduce carbon emissions through technology development, renewable energy use, carbon pricing mechanisms, research and development, circular economy practices, energy management systems, and collaboration between industry, government, and academia. This policy assertion seeks to encourage the development and implementation of technologies that can reduce carbon emissions in steel manufacturing processes, such as hydrogen-based steelmaking, carbon capture and utilization, and energy-efficient processes. Low-carbon technologies, renewable energy, a carbon price, material efficiency, and collaboration are key strategies to reduce carbon emissions in the steel sector. Low-carbon energy sources such as wind and solar can be used to power the steelmaking process, while carbon pricing can reduce industrial emissions. To reduce emissions, stakeholders from all stages of the value chain must collaborate to develop decarbonization strategies, such as funding R&D, exchanging knowledge, and offering carbon-cutting incentives. This review provides a conceptual design approach proposed for the successful analysis of steel decarbonization potential from a process system engineering perspective. Challenges and opportunities are also been highlighted with respect to energy, economics, and environmental aspect. Technologies still require more advancement in terms of operation and energy intensity as technical and economic aspects are found superior to conventional technologies.Web of Science347art. no. 12845

Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds

Conventional leak detection techniques require improvements to detect small leakage (<10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure and mass flow rate signals at the pipeline inlet were used to estimate mass flow rate at the outlet under leak free conditions using Hammerstein model. These signals were further used to define adaptive thresholds to separate leakage from normal conditions. Unlike past studies, this work successfully detected leakage under transient conditions in an 80-km pipeline. The leakage detection performance of the proposed methodology was evaluated for several leak locations, varying leak sizes and, various signal to noise ratios (SNR). Leakage of 0.15 kg/s—3% of the nominal flow—was successfully detected under transient boundary conditions with a F-score of 99.7%. Hence, it can be concluded that the proposed methodology possesses a high potential to avoid false alarms and detect small leaks under transient conditions. In the future, the current methodology may be extended to locate and estimate the leakage point and size

Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds

Conventional leak detection techniques require improvements to detect small leakage (&lt;10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure and mass flow rate signals at the pipeline inlet were used to estimate mass flow rate at the outlet under leak free conditions using Hammerstein model. These signals were further used to define adaptive thresholds to separate leakage from normal conditions. Unlike past studies, this work successfully detected leakage under transient conditions in an 80-km pipeline. The leakage detection performance of the proposed methodology was evaluated for several leak locations, varying leak sizes and, various signal to noise ratios (SNR). Leakage of 0.15 kg/s&mdash;3% of the nominal flow&mdash;was successfully detected under transient boundary conditions with a F-score of 99.7%. Hence, it can be concluded that the proposed methodology possesses a high potential to avoid false alarms and detect small leaks under transient conditions. In the future, the current methodology may be extended to locate and estimate the leakage point and size

Tetracyanoborate anion-based ionic liquid for natural gas sweetening and DMR-LNG process: Energy, exergy, environment, exergo-environment, and economic perspectives

In this study, less viscous 1-hexyl-3-methylimidazolium tetracyanoborate is used to capture CO2 from the natural gas. The obtained sweet gas was liquefied through dual mixed refrigerant technology. Energy, exergy, environment, exergo-environment, and economic analysis were performed to estimate the feasibility of the process. Energy of CO2 capture process was optimized through artificial neural network using feed forward model of 2*10*3 structural design. The results revealed that the carbon capture process provides 82.1 % of exergy efficiency while MEA-based process has 58 %. The NG liquefaction process was optimized based on three case studies using 3, 4, and 5 combinations of mixed refrigerants (MRs) to minimize the specific energy con-sumption (SEC). The results indicated that the combination of 5 MRs provide a least SEC of 9.6 kW/kmol of LNG. Furthermore, proposed process shows less specific CO2 emissions of 0.91 kg of CO2/kmol with a carbon capture rate of &gt;= 99 %. Exergo-environment analysis reveals that the proposed process is an environmentally benign process in terms of environmental effectiveness and exergy stability factors. Economic analysis of this process provides total cost savings of 62.7 %, 55.37 %, and 52.77 % relative to MEA in combination of 3, 4 and 5 MRs, respectively

Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis

[Image: see text] The chemical process industry has become the backbone of the global economy. The complexities of chemical process systems have been increased in the last two decades due to online sensor technology, plant-wide automation, and computerized measurement devices. Principal component analysis (PCA) and signed directed graph (SDG) are some of the quantitative and qualitative monitoring techniques that have been widely applied for chemical fault detection and diagnosis (FDD). The conventional PCA-SDG algorithm is a single-scale FDD representation origin, which cannot effectively solve multiple FDD representation origins. The multiscale PCA-SDG wavelet-based monitoring technique has potential because it easily distinguishes between deterministic and stochastic characteristics. This study uses multiscale PCA-SDG to detect, diagnose the root cause and identify the fault propagation path. The proposed method is applied to a continuous stirred tank reactor system to validate its effectiveness. The propagation route of most process failures is detected, identified, and diagnosed, which is well-aligned with the fault description, demonstrating a satisfactory performance of the suggested technique for monitoring the process failures

A Comprehensive Review on Thermal Coconversion of Biomass, Sludge, Coal, and Their Blends Using Thermogravimetric Analysis

Lignocellulosic biomass is a vital resource for providing clean future energy with a sustainable environment. Besides lignocellulosic residues, nonlignocellulosic residues such as sewage sludge from industrial and municipal wastes are gained much attention due to its large quantities and ability to produce cheap and clean energy to potentially replace fossil fuels. These cheap and abundantly resources can reduce global warming owing to their less polluting nature. The low-quality biomass and high ash content of sewage sludge-based thermal conversion processes face several disadvantages towards its commercialization. Therefore, it is necessary to utilize these residues in combination with coal for improvement in energy conversion processes. As per author information, no concrete study is available to discuss the synergy and decomposition mechanism of residues blending. The objective of this study is to present the state-of-the-art review based on the thermal coconversion of biomass/sewage sludge, coal/biomass, and coal/sewage sludge blends through thermogravimetric analysis (TGA) to explore the synergistic effects of the composition, thermal conversion, and blending for bioenergy production. This paper will also contribute to detailing the operating conditions (heating rate, temperature, and residence time) of copyrolysis and cocombustion processes, properties, and chemical composition that may affect these processes and will provide a basis to improve the yield of biofuels from biomass/sewage sludge, coal/sewage sludge, and coal/biomass blends in thermal coconversion through thermogravimetric technique. Furthermore, the influencing factors and the possible decomposition mechanism are elaborated and discussed in detail. This study will provide recent development and future prospects for cothermal conversion of biomass, sewage, coal, and their blends