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

Plant-wide MPC control scheme for CO2 absorption/stripping system

The high contents of CO2 in natural gas processing industries cause various issues in the operation, and it is essential to reduce the amount of CO2 using amine-based absorption processes. An efficient and flexible control strategy is highly desirable for CO2 removal in CO2 absorption/stripping system. The excellent performance of Model Predictive Control (MPC) in setpoint tracking and disturbance rejection scenarios could make it a better option for the flexible controllability of a CO2 absorption/stripping plant. MPC performance depends significantly on the accuracy of the identified mathematical model of the plant. The state-space model is believed as the better option for MPC as it represents the plant model with true dynamics. Therefore, this study is focused on the design of the 2 × 2 MPC control strategy in MATLAB® MPC Designer Toolbox using 2nd order continuous-time state-space model. The main aim of this study is to develop a plant-wide control scheme based on MPC for the natural gas absorption/striping system. Step changes in the CO2 composition of sweet gas (±5%) and stripper temperature (±15%) have been introduced in the absorption/stripping simulation model. The results show that the MPC controller has achieved the new setpoint of CO2 composition within 0.5 sec in the setpoint tracking scenario. Similarly, the MPC controller has been able to reject the disturbances successfully introduced as ± 15% step change in stripper temperature within 7.5 sec. Hence, the performance of the MPC controller using the state-space model at higher step changes is adequate with no peak, closer to the setpoint, and no overshoot in the output

Plant-wide MPC control scheme for CO2 absorption/stripping system

The high contents of CO2 in natural gas processing industries cause various issues in the operation, and it is essential to reduce the amount of CO2 using amine-based absorption processes. An efficient and flexible control strategy is highly desirable for CO2 removal in CO2 absorption/stripping system. The excellent performance of Model Predictive Control (MPC) in setpoint tracking and disturbance rejection scenarios could make it a better option for the flexible controllability of a CO2 absorption/stripping plant. MPC performance depends significantly on the accuracy of the identified mathematical model of the plant. The state-space model is believed as the better option for MPC as it represents the plant model with true dynamics. Therefore, this study is focused on the design of the 2 × 2 MPC control strategy in MATLAB® MPC Designer Toolbox using 2nd order continuous-time state-space model. The main aim of this study is to develop a plant-wide control scheme based on MPC for the natural gas absorption/striping system. Step changes in the CO2 composition of sweet gas (±5%) and stripper temperature (±15%) have been introduced in the absorption/stripping simulation model. The results show that the MPC controller has achieved the new setpoint of CO2 composition within 0.5 sec in the setpoint tracking scenario. Similarly, the MPC controller has been able to reject the disturbances successfully introduced as ± 15% step change in stripper temperature within 7.5 sec. Hence, the performance of the MPC controller using the state-space model at higher step changes is adequate with no peak, closer to the setpoint, and no overshoot in the output

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

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