Process Safety and Environmental Protection最新文献

Although technically challenging, effective, safe, and economical transport is crucial for enabling a widespread rollout of hydrogen technologies. A promising option to transport large amounts of hydrogen lies in employing retrofitted natural gas pipelines. Nevertheless, H₂-rich environments tend to degrade pipeline steels, reducing their load-bearing capability and accelerating crack propagation. Regular inspection and maintenance activities can preserve the pipelines’ integrity and guarantee safe operations. The risk-based inspection (RBI) approach is based on estimating the risk for each component item. It focuses most inspection activities on high-risk components to reduce costs while maximizing the plant’s safety and availability. However, the RBI standards do not consider hydrogen-induced degradations and cannot be adopted for industrial equipment operating in H₂ environments. This study proposes a novel ad-hoc methodology for the risk-based inspection planning of hydrogen handling equipment. A machine-learning model to predict the fatigue crack growth in gaseous hydrogen environments is developed and integrated with the conventional RBI approach. The proposed methodology is validated on three pipelines transporting hydrogen and natural gas in different concentrations. The results show how similar operating conditions can determine different degradation rates depending on the environment and highlight how hydrogen-enhanced fatigue can reduce the pipelines’ lifetime.

With the development of the new energy industry, battery life and rapid charge-discharge capacity have attracted much attention. At the same time, the high temperature inside the cell during high-rate charging and discharging may increase the probability of the battery thermal runaway. This paper studied the thermal runaway reaction of Li-ion batteries under different state of charge (SOC) and charge rates using a self-made experimental platform. The experimental phenomena and the changes in the temperature field were recorded. The key parameters, such as trigger temperature (T_1, Lithium battery back thermal runaway triggers temperature), maximum temperature (T_max),voltage, and mass loss (ML) of thermal runaway, were measured. The morphology changes of electrode materials, the battery remains, and the dynamics of thermal runaway reaction after high rate charge and discharge were further analyzed. The results show that for the 4 C-100 % battery, the T₁ and E_a are reduced by 22.6 ℃ and 82.2 %, and the T_max and maximum mass loss rate (MLR_max) are increased by 218.14 ℃ and five times, compared with the 1 C-50 % battery. With the increase of charge-discharge rate, the thermal stability of the battery decreases, and the gravity degree of accident increases.

Background

Cogeneration power plants traditionally rely on fossil fuels to produce stable power and heat. However, increasing energy demand and population growth have intensified the emission of biological pollutants due to fossil fuel use. The Global Alliance on Health and Pollution advocates for integrating renewable energy sources to mitigate these issues.

Objectives

This study aims to evaluate the integration of a solar-biomass polygeneration system with a hybrid solar-waste-fossil fuel cogeneration system. The goal is to analyze the system from technical, economic, and environmental perspectives, focusing on optimizing energy demand and minimizing environmental impact.

Methods

To assess energy demand and supply, the R-curve methodology was applied to the hybrid cogeneration system, with a specific focus on solar and biomass renewable energies. Various scenarios were analyzed, including total annual costs, pollutant emissions, water footprint, and overall environmental impact based on life cycle assessment. The study examined and compared the performance of three types of biomass waste (Municipal solid waste, mixed paper waste, and date palm waste). Multi-objective optimization was performed using artificial intelligence and machine learning techniques, employing four meta-heuristic algorithms. The conditions generated by each algorithm were analyzed and compared.

Results

Municipal solid waste, being the most readily available fuel, provided the most favorable economic conditions for the system. Environmentally, municipal solid waste ranked in the middle compared to other fuels. Among the optimization algorithms, the Salps swarm algorithm proved to be the most efficient in terms of calculation time and system efficiency improvements. The optimization improved net power generation by 5.25 %, overall energy efficiency by 16.27 %, total cost rate by 10.19 %, and total environmental impact rate by 14.02 %.

Conclusion

The integrated system's performance was analyzed across different climatic change throughout the year. The multi-objective Salps swarm algorithm optimization demonstrated significant benefits in enhancing system efficiency and reducing costs and environmental impacts.

High-pressure steam and hot water often coexist as industrial waste heat. In this study, dual loop and single loop ORC systems are designed for 700 kPa, 4.1 kg/s steam, and 90 ℃, 122.36 kg/s hot water conditions to study the off-design performance when steam or hot water conditions change. To maximize net output power, we employ a particle swarm optimization algorithm to optimize the evaporation and condensation temperatures. The results show that within the specified hot water conditions, the evaporation and condensation temperatures of D-ORC's low-pressure loop and S-ORC increase with rising hot water inlet temperature and flow rate. The S-ORC demonstrates a higher net output power growth rate as hot water flow rate and temperature rise. Under specific steam conditions, when the steam outlet is in a gas-liquid two-phase state, D-ORC's maximum net output power is 1.7 % higher than that of the S-ORC, with little variation in optimal evaporation and condensation temperatures with respect to steam inlet pressure. At a 3.5 kg/s steam flow rate, the D-ORC's high-pressure loop becomes ineffective, whereas S-ORC efficiently adjusts heat exchange capacity under diverse steam-water conditions, Consequently, the D-ORC's average net output power is 34.2 % lower than that of the S-ORC.

The production of polyvinyl chloride (PVC) encounters challenges stemming from the temporal and spatial coupling characteristics inherent in the fixed bed ethylene oxychlorination process. Consequently, the implementation of enhanced safety measures and risk reduction strategies becomes imperative. This study introduces a pioneering methodology leveraging a spectral temporal graph neural network. By leveraging reactor temperature data, spatial variable decoupling facilitated by the Fourier transform, and a self-attentive mechanism within graph neural networks, the proposed approach adeptly forecasts future reactor states. The model's seamless alignment with the physical knowledge of reaction processes, validated through the adjacency matrix and hotspot region identification, underscores its efficacy in ensuring process safety and mitigating operational risks in PVC production. Empirical findings further validate the effectiveness of the approach, with predictions demonstrating an error margin of less than 0.5°C in forecasting future reactor temperatures.

Harmful Microcystis blooms and microcystins have become a major hidden threat to the safety of the water environment. The application of enzymatic degradation of microcystins has been severely limited by the complex environment. In this study, chitosan-graphene (CG), prepared from green biomass, was employed as matrix material, loaded with 100–200 nm Fe₃O₄ nanoparticles (MCG) and immobilized microcystinase A (MlrA@MCG). The preparation of MlrA@MCG was characterized by scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, vibrating sample magnetometer and fluorescence labelling. The results of the activity analysis demonstrated that MlrA@MCG exhibited superior degradation activity for MCs, as well as enhanced heat and alkaline resistance in comparison to free MlrA. Furthermore, MlrA@MCG can be recovered simply by means of a magnetic field, and its activity remains at 48.6 % after 10 repeated uses. More importantly, MlrA@MCG and the degradation products of MC-LR were not found to be cytotoxic to human cells. It is interesting that the immobilization of MlrA resulted in a reduction in the cytotoxicity of MCG. 0.2 U of MlrA@MCG can still degrade MC-LR from 232.64 μg L⁻¹ to 94.39 μg L⁻¹ in water from simulated severe Microcystis blooms within 24 h, showing excellent catalytic activity and stability. The study proposed a secure and efficacious approach for the elimination of microcystins from harmful Microcystis blooms, offering a promising avenue for the improvement of environmental safety.