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In the context of carbon neutrality, energy conservation and carbon reduction in the heating industry have become the focus of attention. However, severe heat loss is currently occurring in the secondary heating system due to hydraulic imbalance, which is unable to supply heat according to users' needs. In view of this, a thermodynamic-hydraulic model of users' demand for heat and flow rate for the heating system is established. Then, a novel method to regulate hydraulic imbalance is proposed, with the dynamic heat demand satisfied by regulating the valve opening based on NSGA-II. The optimization results show that the comprehensive hydraulic imbalance coefficient has been reduced from 23.08 to less than 0.019, which means that almost 100% of users can get heat supplies corresponding to their needs, while the energy consumption is reduced by about 30%. Additionally, when there are 1000 population and iteration steps, good convergence and high speed are presented using this method. In summary, an on-demand optimized model of hydraulic balance for the secondary heating network is put forward, which provides powerful engineering support and guidance for intelligent heating construction. • This paper presents a method to regulate the hydraulic balance of network using NSGA-II according to the heat demand. • This paper combines the demand flow regulation of users with the resistance analysis of the heating network. • The hydraulic balance can be implemented greatly by using the hydraulic imbalance coefficient as the optimization goal. • The number of iteration steps in the algorithm has an effect on the optimization results. [ABSTRACT FROM AUTHOR]

Due to high material density, high energy consumption density and CO 2 emission density, it is not only difficult but significant to clarify the relationship between energy consumption, the CO 2 emission and the production cost in different conditions. However, the previous researches rarely refer how to balance the energy consumption, the CO 2 emission and the production cost after the fluctuation of material, energy and carbon price as well as what will happen to them if production structure changes. Therefore, based on the conservation law of mass and energy, to study iron and steel manufacturing process (ISMP), this paper, taking carbon price into consideration, establishes a muti-optimization model of energy consumption, CO 2 emission and cost. After optimization with different objectives, the production cost per tonne of crude steel is reduced by 192.03 CNY (7.71%), the CO 2 emission per tonne of crude steel is reduced by 224.22 kg (13.37%), and the energy consumption per tonne of steel is reduced by 51.20 kgce (9.10%). Moreover, based on the optimization results under different objectives, it is ironmaking process (coal ratio and ore ratio) and steelmaking process (amount of scrap steel) that has more impact on three above as well as ore blending and coal blending have a great influence on production cost but little effect on energy consumption and CO 2 emission. • The muti-objective optimization model of iron and steel industry is established. • The carbon price is considered during model establishment and analyzing. • The objective with energy consumption, CO 2 emissions and cost are optimized. • The factors affecting cost, energy consumption and CO 2 emission are discussed. [ABSTRACT FROM AUTHOR]

The integrated optimization and analysis of material and energy flows are effective ways to reduce energy consumption and costs in the iron and steel sector. There is still a lack of research on the multi-objective optimization of the integrated steel production process and the auxiliary energy process. To fill this gap, this paper presented a single- and multi-objective optimization model for material and energy scheduling and utilization of the steelworks. This model considered physical and chemical conversion mechanisms in the production process, as well as operating characteristics and production-consumption relations of equipment in the gas-steam-electricity network. The objective function was constructed using comprehensive energy consumption (CEC) and economic cost (EC), and fuzzy sets were used to find a compromise solution and the trade-off operation strategies between the two goals. The CEC and EC effectively dropped by 5.09% and 7.29%, respectively, in comparison to the actual enterprise value, through the optimization of the material ratio, product structure, energy utilization, and other parameters of the steelworks. Additionally, a quantitative analysis of the effects of variables such as the ratio of pellets, the ratio of scrap, the hot charging temperature of the slab, and the utilization of surplus gases was conducted. • A single- and multi-objective optimization model for material and energy scheduling and utilization is proposed. • The compromise solution of energy consumption and economic multi-objective optimization is found. • Influence of material and energy flows on variation in multi-objective is discussed. • Highly efficient and cost-effective modes of using surplus gases are explored. [ABSTRACT FROM AUTHOR]