Exploring theoretical energy consumption introduces a fresh perspective for energy-saving research within the iron and steel industry, with a primary focus on the energy expended during material transformation. Building upon the theory of theoretical energy consumption, this study meticulously investigates the theoretical energy consumption associated with each stage of the iron and steel making process, including coking, sintering, pelletizing, ironmaking, steelmaking, and hot rolling. The findings reveal that, under specific conditions, the theoretical energy consumption for each process is as follows: coking (2.59 GJ), sintering (1.36 GJ), pelletizing (1.02 GJ), ironmaking (8.81 GJ), steelmaking (−0.16 GJ), and hot rolling (0.76 GJ). Additionally, this study delves into the analysis of influencing factors on theoretical energy consumption. Using the coking process as an illustrative example, it is observed that the theoretical energy consumption in coking decreases with a reduction in both moisture and volatile content in coal. Under the specified conditions, the minimum theoretical energy consumption for each process is as follows: coking (2.51 GJ), sintering (0.98 GJ), pelletizing (0.67 GJ), ironmaking (8.38 GJ), steelmaking (−0.58 GJ), and hot rolling (0.07 GJ), respectively. This comprehensive analysis serves as a valuable resource for advancing sustainable practices in the iron and steel industry.

The coordinated optimization of energy conservation, efficiency improvement, and pollution reduction in the sintering production process is vital for the efficient and sustainable development of the sintering department. However, previous studies have shown shortcomings in the multi-objective collaborative optimization of sintering systems and the quantification of pollutant impacts. To address these, this paper proposes a multi-objective optimization method integrated with the NSGA-III algorithm and establishes an integrated system optimization model for sintered ore production and high-temperature waste heat recovery. The results demonstrate significant improvements: energy utilization efficiency increased by 0.67%, energy consumption decreased by 17.3 MJ/t, production costs were reduced by 11.45 CNY/t, and the emissions of CO₂, SO₂, and NO_x were reduced by 0.464 kg/t, 0.034 kg/t, and 0.008 kg/t, respectively. Additionally, the study identified optimal configuration parameters and analyzed the quantitative impact of several key factors on multiple indicators. The results also show that reducing the water content of the mixture, decreasing the middling coal content in the fuel, and increasing the thickness of the material layer are effective strategies to reduce energy consumption and pollutant emissions in the sintering process. Overall, implementing these optimizations can bring significant economic and environmental benefits to the steel industry.

To mitigate the significant impact of system nonlinearities, time-varying parameters, and external load disturbances on the output force of hydraulic servo systems in active hydraulic suspensions for engineering vehicles, this study proposes a beetle swarm optimization (BSO)-optimized extended state observer (ESO)-based sliding mode control (SMC) strategy. A comprehensive mathematical model of the hydraulic servo system is established, and an ESO-based SMC controller is designed, taking into account the coupled effects of chamber pressure dynamics and external loads on the uncertain output force. The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov stability theory. The effectiveness of the proposed control strategy is verified through both numerical simulations and experimental tests. For step inputs of 5000 N and 8000 N, overshoot is significantly reduced compared with the conventional proportional–integral–derivative control and the standard extended state observer-based sliding mode control, while the settling time is shortened by more than 65% in simulations and up to 75% in experiments. Under sinusoidal force excitations at frequencies of 0.5 Hz, 1 Hz, and 2 Hz, the maximum tracking error, mean error, and standard deviation of the tracking error are substantially reduced, with the maximum error reduction exceeding 90%. These results demonstrate that the proposed method achieves high-precision force tracking under external disturbances and pronounced system uncertainties, providing an effective solution for force control of hydraulic servo systems in active suspension applications for engineering vehicles.

To support systematic carbon-reduction decision-making in integrated steelworks, this study develops a bottom-up framework that combines process-based mechanisms with data-driven regression for the blast furnace–basic oxygen furnace (BF–BOF) route. The framework links material, energy, exergy, carbon, and economic value flows and formulates a multi-objective optimization problem that minimizes exergy intensity and CO₂ emission intensity while maximizing the cost–benefit ratio (CBR) per tonne of steel product. For the representative 380CL product, the selected optimized solution reduces CO₂ emissions from 2.082 to 1.929 t CO₂/t-SP (7.35%) and exergy consumption from 19.175 to 18.027 GJ/t-SP (5.99%) while increasing the CBR from 0.732 to 0.771 (5.33%). One-factor-at-a-time sensitivity analysis shows that material blending, moisture content, coke and scrap ratios, blast conditions, and interface temperatures can have different environmental and economic effects under the assumed plant conditions. The methodological contribution is the product-level coupling of exergy, carbon, and economic value flows in a bottom-up BF–BOF model, rather than the individual identification of already-known process effects. The quantitative results are specific to the case enterprise, product, equipment configuration, material qualities, energy and carbon prices, and operating constraints. The framework should not be directly transferred to electric-arc-furnace (EAF) steelmaking without route-specific reformulation; validation across additional plants and products is needed.