Scipediacontent: Scipediacontent moved page Review 515154621796 to Ju et al 2026a

2026-06-12T08:02:01Z

Scipediacontent moved page Review 515154621796 to Ju et al 2026a

Scipediacontent: Scipediacontent moved page Draft content 814451670 to Review 515154621796

2026-06-12T07:52:07Z

Scipediacontent moved page Draft content 814451670 to Review 515154621796

Scipediacontent: Created page with " == Abstract ==
To address the multi-objective collaborative optimization of quality, energy consumption, and yield under dynamic conditions in the Portland cement combine..."

2026-06-12T07:52:04Z

Created page with " == Abstract == <p>To address the multi-objective collaborative optimization of quality, energy consumption, and yield under dynamic conditions in the Portland cement combine..."

New page

== Abstract ==

<p>To address the multi-objective collaborative optimization of quality, energy consumption, and yield under dynamic conditions in the Portland cement combined grinding process, this paper proposes a novel algorithm, CGDS-LTL, based on cooperative game theory and temporal perception. First, a hybrid temporal model combining Linformer, TCN, and LSTM was developed to dynamically track process conditions in the Portland cement combined grinding process. Second, an optimization objective function was established, and a cooperative game theory framework was introduced to address the challenge of achieving multi-objective optimization, which could not be effectively solved with a single paretooptimal solution. Meanwhile, volatility metrics were used to quantify the adjustment range of operational variables, allowing for the dynamic optimization of decision constraints. This approach mitigated the deviation of the pareto front from current decision settings caused by high population randomness, ultimately identifying the optimal solution for the multiobjective collaborative optimization problem. Finally, experiments using real production data from a Portland cement plant demonstrated that, compared with NSGA-II and C-TAEA, the proposed method improved the hypervolume indicator by 95% and 33.3%, respectively, indicating a more uniform solution distribution and better convergence. This demonstrated the interpretability and effectiveness of the proposed framework for dynamic multi-objective optimization in Portland cement combined grinding.OPEN ACCESS Received: 14/04/2026 Accepted: 21/05/2026</p>

== Document ==
<pdf>Media:Draft_content_814451670-5702-document.pdf</pdf>

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Ju et al 2026a - Revision history

Scipediacontent: Scipediacontent moved page Review 515154621796 to Ju et al 2026a

Scipediacontent: Scipediacontent moved page Draft content 814451670 to Review 515154621796

Scipediacontent: Created page with " == Abstract == To address the multi-objective collaborative optimization of quality, energy consumption, and yield under dynamic conditions in the Portland cement combine..."

Scipediacontent: Created page with " == Abstract ==
To address the multi-objective collaborative optimization of quality, energy consumption, and yield under dynamic conditions in the Portland cement combine..."