Energy and exergy recovery from exhaust hot water using organic Rankine cycle and a retrofitted configuration

Exhaust hot water （EHW） is widely used for various industrial processes. However, the excess heat carried by EHW is typically ignored and discharged into the environment, resulting in heat loss and heat pollution. An organic Rankine cycle （ORC） is an attractive technology to recycle heat from low-temperature energy carriers. Herein, ORC was used to recycle the heat carried by EHW. To investigate the energy and exergy recovery effects of EHW, a mathematical model was developed and a parametric study was conducted. The energy efficiency and exergy efficiency of the EHW-driven ORC system were modeled with R245fa, Rl13 and R123 as the working fluids. The results demonstrate that the EHW and evaporation temperatures have significant effects on the energy and exergy efficiencies of the EHW-driven ORC system. Under given EHW conditions, an optimum evaporation temperature exists corresponding to the highest exergy efficiency. To further use the low-temperature EHW, a configuration retrofitted to the ORC by combining with flash evaporation （FE） was conducted. For an EHW at 120 ~C and 0.2 MPa, the maximum exergy efficiency of the FE-ORC system is 45.91% at a flash pressure of 0.088 MPa. The FE-ORC performs better in exergy efficiency than the basic FE and basic EHW-driven ORC.

Multi-energy synergistic optimization in steelmaking process based on energy hub concept

The production process of iron and steel is accompanied by a large amount of energy production and consumption. Optimal scheduling and utilization of these energies within energy systems are crucial to realize a reduction in the cost, energy use, and CO_2 emissions.However, it is difficult to model and schedule energy usage within steel works because different types of energy and devices are involved. The energy hub(EH), as a universal modeling frame, is widely used in multi-energy systems to improve its efficiency, flexibility, and reliability.This paper proposed an efficient multi-layer model based on the EH concept, which is designed to systematically model the energy system and schedule energy within steelworks to meet the energy demand. Besides, to simulate the actual working conditions of the energy devices, the method of fitting the curve is used to describe the efficiency of the energy devices. Moreover, to evaluate the applicability of the proposed model, a case study is conducted to minimize both the economic operation cost and CO_2 emissions. The optimal results demonstrated that the model is suitable for energy systems within steel works. Further, the economic operation cost decreased by 3.41%, and CO_2 emissions decreased by approximately 3.67%.

Environmental impacts of domestic resource extraction in China

Introduction:The rapid development of economy has led to extensive raw material consumption and relevant environmental damage in China.To analyze environmental impacts and identify materials resulting in these environmental effects via raw material extraction,we combine economy-wide material flow accounting and life-cycle analysis methods to estimate environmental impacts of Chinese domestic extraction(DE)during the period of 1992–2015.The relationship between these increasing environmental impacts and Chinese GDP was also explored by decoupling analysis.Outcomes:Results show that Chinese DE increased by 372%during 1992–2015.The global warming potential,abiotic depletion potential,and respiratory inorganics of Chinese DE increased by 195%,46%,and 408%,respectively.In terms of specific materials,extraction of iron ores,gravel and sand,and coal induced the most environmental impacts.The relationship between environmental impacts and Chinese GDP/DE was characterized by relative decoupling.Conclusion:To minimize the environmental impacts of extraction,we recommend that the Chinese government improve its extraction techniques and reduce excess demand for materials with large extraction such as iron ores,gravel and sand,and coal.We also recommend researching alternative materials for scarce resources like molybdenum,gold,and fluorite.

Temporal and spatial variation in the environmental impacts of China’s resource extraction at the provincial scale

Introduction:Over the past two decades,China has experienced rapid economic development,which has not only led to a rapid increase in the use of raw materials but has also created environmental problems.This research analyzes the environmental impacts of resource extraction in China at the provincial level,and fully considers the environmental impact of various resources extraction.In addition,it is the first time to quantitatively study the spatial pattern and evolution characteristics of the environmental impacts of China’s resource extraction from multiple perspectives by means of spatial visualization.Outcomes:The results showed that the center of gravity of abiotic depletion potential(ADP)moved northwest,respiratory inorganics(RI)moved southwest and global warming potential(GWP)moved west.The results of the standard deviation ellipse showed that RI and GWP varied over time and space,while ADP showed a discrete trend.In addition,the distribution of the four in the northeast-southwest direction became more prominent.Conclusion:To mitigate the environmental impacts of resource extraction,we recommend that regional governments implement measures to control environmental impacts in the provinces within the distributed ellipse and design targeted policies based on actual conditions.

Prediction of blast furnace gas generation based on data quality improvement strategy

The real-time energy flow data obtained in industrial production processes are usually of low quality.It is difficult to accurately predict the short-term energy flow profile by using these field data,which diminishes the effect of industrial big data and artificial intelligence in industrial energy system.The real-time data of blast furnace gas(BFG)generation collected in iron and steel sites are also of low quality.In order to tackle this problem,a three-stage data quality improvement strategy was proposed to predict the BFG generation.In the first stage,correlation principle was used to test the sample set.In the second stage,the original sample set was rectified and updated.In the third stage,Kalman filter was employed to eliminate the noise of the updated sample set.The method was verified by autoregressive integrated moving average model,back propagation neural network model and long short-term memory model.The results show that the prediction model based on the proposed three-stage data quality improvement method performs well.Long short-term memory model has the best prediction performance,with a mean absolute error of 17.85 m3/min,a mean absolute percentage error of 0.21%,and an R squared of 95.17%.

Plant-Wide Supply-Demand Forecast and Optimization of Byproduct Gas System in Steel Plant

Considerable energy is consumed during steel manufacturing process. Byproduct gas emerges as secondary energy in the process; however, it is also an atmospheric pollution source if it is released into the air. Therefore, the optimal utilization of byproduct gas not only saves energy but also protects environment. To solve this issue, a fore- cast model of gas supply, gas demand and surplus gas in a steel plant was proposed. With the progress of energy conservation, the amount of surplus gas was very large. In a steel plant, the surplus gas was usually sent to boilers to generate steam. However, each boiler had an individual efficiency. So the optimization of the utilization of surplus gas in boilers was a key topic. A dynamic programming method was used to develop an optimal utilization strategy for surplus gas. Finally, a case study providing a sound confirmation was given.

Optimization and Scheduling of Byproduct Gas System in Steel Plant

A mathematical model was proposed to optimize byproduct gas system and reduce the total cost. The scope and boundaries of the system were also discussed at the same time. Boilers and gasholders were buffer users to solve the fluctuation of byproduct gases. The priority of gasholders should be ranked the last. The allocation of surplus ga- ses among gasholders and boilers was also discussed to make full use of gases and realize zero emission targets. Case study shows that the proposed model made good use of byproduct gases and at least 7.8 ~//00 operation cost was re- duced, compared with real data in iron and steel industry.

Energy Consumption and Its Influencing Factors of Iron and Steel Enterprise

Based on the theory of system energy-saving, a multilevel input-output computational model ot the iron and steel enterprise was established. And one example was calculated using this model to analyze the product energy value, the process of energy consumption and energy consumption per ton of steel of an iron and steel enterprise. The influences of factory layout, steel ratio and production structure are calculated and analyzed. The calculation example indicates that reasonable factory layout is helpful to reduce its transportation energy consumption, decrease loss of heat, improve product rate and reduce environmental contamination; ore to steel ratio and iron to steel ratio decrease at the same degree; the influence of ore to steel ratio on energy intensity per ton is less than that of iron to steel ratio. The lord process product structure has a certain effect on comprehensive energy consumption per ton of steel.

Carbon neutrality needs a circular metal-energy nexus

Carbon neutrality requires systematic transformations of both energy and metal systems.These transformations are not isolated but rather interlinked and interdependent,such that trade-offs between different strategies exist.Herein,we explore the critical interlinkages between energy and metal systems and further propose a circular metal-energy nexus to advance global coordinated actions towards a carbon-neutral future.

Dynamic forecasting and optimal scheduling of by-product gases in integrated iron and steel works

The by-product gases, which are generated in ironmaking, coking and steel making processes, can be used as fuel for the metallurgical processes and on-site power plants. However, if the supply and demand of by-product gases are imbalanced, gas flaring may occur, leading to energy wastage and environmental pollution. Therefore, optimal scheduling of by-product gases is important in iron and steel works. A BP_LSSVM model, which combines back-propagation (BP) neural network and least squares support vector machine (LSSVM), and an improved mixed integer linear programming model were proposed to forecast the surplus gases and allocate them optimally. To maximize energy utilization, the stability of gas holders and boilers was considered and a concise heuristic procedure was proposed to assign penalties for boilers and gas holders. Moreover, the optimal level of gas holder was studied to enhance the stability of the gas system. Compared to the manual operation, the optimal results showed that the electricity generated by the power plant increased by 2.93% in normal condition and by 22.2% in overhaul condition. The proposed model minimizes the total cost by optimizing the boiler load with less adjustment frequency and the stability of gas holders and can be used as a guidance in dynamic forecasting and optimal scheduling of by-product gases in integrated iron and steel works.

Value chain and statistical entropy analyses based on iron flows in China during 1990-2015

The iron and steel industry in China has experienced vast changes over the past thirty years.To have a precise knowledge of the circumstances behind its evolution,it is essential to perform an iron flow analysis.Accordingly,iron flow analysis for the years 1990-2015 was conducted.Firstly,the iron natural resource efficiency,Chinese steel scrap index,and Chinese iron ore support ratio which can reflect the running status of China's iron and steel industry for these six years(1990,1995,2000,2005,2010,and 2015)were analyzed;thereafter,value chain and statistical entropy analyses were conducted based on the iron flow analysis,and some interesting results were obtained.Discussions and conclusions based on the results along with the recommendations for the China's iron and industry were proposed.

Pretreatment of metallurgical sewage via vacuum distillation driven by low-temperature exhausted gas from steel plants

The metallurgical sewage has very complex component and a significant environmental perniciousness and needs high treatment costs. In addition, too much low-temperature waste heat is emitted owing to the lack of suitable users. Considering these concerns, a low-temperature-driven pretreatment method via vacuum distillation was proposed to treat the sewage from the metallurgical production. It uses the sensible heat carried by low-temperature exhausted gases to drive the distillation of sewage. The distilled water can be reused into the process as new water supply, while the enriched wastewater is discharged into the sewage treatment center for subsequent treatment. Converter dust removal sewage was chosen to perform an experimental observation. The variations of chemical oxygen demand, ammonia nitrogen, suspended solids, electrical conductivity, and pH of the condensate under different vacuum degrees and evaporation rates were mainly investigated. It can be found that the quality of the condensate gets better under certain conditions, which validates the feasibility of the proposed approach. Furthermore, by comprehensively analyzing the water quality indices and their influencing factors, the optimal vacuum degree was suggested to be controlled between 0.07 and 0.09 MPa, and the best evaporation rate was between 40 and 60%.