Acta Energiae Solaris Sinica•2026•DOI: 10.19912/j.0254-0096.tynxb.202608_9702
Short-term load forecasting faces escalating volatility and nonlinearity due to high renewable penetration. This study proposes a hybrid framework integrating Soft Dynamic Time Warping-Improved Partitioning Around Medoids (SDTW-IPAM) clustering with an Informer model. The SDTW distance metric captures local temporal deformations in load curves, while Gap statistics and K-means++ initialization optimize PAM clustering to adaptively determine cluster count and initial medoids. Load profiles are partitioned into double-peak, high-peak, and smooth patterns. Maximum Information Coefficient (MIC) selects differential features for each cluster, and dedicated Informer models are trained per pattern. Validation on real load data from Urumqi, Xinjiang, demonstrates that the combined model outperforms benchmark models across EMAE, ERMSE, and R², particularly for highly volatile load patterns. The method enhances forecasting accuracy and robustness, offering practical value for power system scheduling under renewable uncertainty. Limitations include exclusion of direct renewable generation, price signals, and storage states; future work will incorporate multi-variable inputs and extreme weather scenarios.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202506020
The rotary kiln roasting of lepidolite for lithium extraction faces challenges of unstable lithium conversion rates and high energy consumption. To address this, a multi-objective optimization method coupling improved neural network simulation with a multi-objective genetic algorithm was proposed, targeting the synergistic optimization of lithium conversion rate (TRLi) and natural gas consumption intensity (EIng). Using long-term industrial time-series data of batching parameters and kiln operating variables, back-propagation (BP) neural network and its particle swarm optimization (PSO) improved variant were developed to model TRLi and EIng. The PSO-BP model demonstrated superior accuracy in capturing the complex nonlinear relationships, reducing mean absolute percentage errors (MAPE) to 0.278 and 0.284 for TRLi and EIng, respectively. Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) was employed to construct a multi-objective optimization model, yielding a Pareto-optimal set of process parameters that maximize TRLi and minimize EIng. The results revealed that under NSGA-II optimized conditions, TRLi could be stabilized between 82.45% and 87.96%, an average increase of 3.61 percentage points over baseline operations, while EIng could be reduced to 53.7 m3 per ton of clinker. For an annual processing capacity of 3.2×105 tons of lepidolite concentrate and sulfate mixture, this corresponds to an additional 127.1 tons of lithium metal recovery, a reduction of 1,964,912 m3 in natural gas consumption, and a decrease of 3,763.84 tons in CO2 emissions annually. This study provides theoretical and technical support for the green, high-quality, and low-carbon supply of critical raw materials for the lithium battery new energy industry.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025092802
Activated carbon (AC) filters in drinking water treatment plants (DWTPs) experience significant adsorption performance decline over extended operation, yet complete media replacement is a major cost. To evaluate cost-effective strategies, pilot-scale column experiments with five AC replacement ratios (0%, 30%, 50%, 70%, and 100%) were conducted to assess removal of disinfection by-product (DBP) precursors and pesticide-related emerging contaminants. For dissolved organic matter (DOM), all fractions except low molecular weight compounds (LMWC) achieved >85% of the removal obtained with full replacement when 70% new AC was used, with UV254 removal reaching 70%. LMWC, due to small molecular size and low adsorption energy, required higher replacement ratios or full replacement for substantial removal. For DBPs, removal of trihalomethanes (THMs) and haloacetic acids (HAAs) was insensitive to replacement ratio, while haloacetaldehydes (HALs) removal improved markedly with increasing ratio, indicating structural selectivity. For pesticide-related contaminants, all except triazoles achieved >95% removal at 70% replacement; triazoles, due to high water solubility, high polarity, and low octanol-water partition coefficient, achieved only ~60% removal. Overall, replacing 70% of AC restored treatment performance to >80% of that with full replacement, ensuring effluent quality while saving ~30% of new carbon cost. Molecular structure, polarity, and pore size matching are key determinants of removal efficiency; optimizing replacement ratio balances water quality and economic benefits.