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| Design and Application of Solvers for Nonlinear and Multidimensional Problems in Paper Production Systems |
| Received:November 14, 2024 |
| DOI：10.11980/j.issn.0254-508X.2025.02.002 |
| Key Words:trust region interior point method multi-start optimization algorithm solver nonlinear optimization |
| Fund Project:中央高校基本科研业务费专项资金资（2023ZYGXZR100）；国家自然科学基金（22478141）。 |
| Author Name | Affiliation | Postcode | | LI Kanghao | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | CHEN Haozhou | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | ZHANG Jie | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | HAN Yulin* | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | MAN Yi* | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 |
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| Abstract:The intelligent transformation of the paper industry involves solving dynamic and real-time problems in numerous high-dimensional mathematical models. Due to the nonlinearity， multidimensionality， and uncertainty inherent in paper production systems， the mathematical models describing these processes often consist of extensive sets of equations. Furthermore， frequent production fluctuations and transitions necessitate efficient and frequent resolution of these complex model sets to meet the demands of dynamic production optimization. Developing solvers tailored to the challenges of paper production models is key to addressing this issue. This study focused on the characteristics of nonlinear， multidimensional solutions in paper production models and designed a global optimization solver for nonlinear， multidimensional paper production systems based on the trust-region interior-point method and TikTak multi-start optimization algorithm. The proposed solver achieves efficient resolution of complex production constraints and uncertain initial conditions. The results showed that the solver achieved a 100% success rate in finding the global optimum in a paper drying section optimization case， with an average solving time of 0.81 per instance， exhibiting high robustness. Additionally， in a paper energy system optimization case， the solver successfully reduced computational resource usage by 59.7% and computation time by 9.29%. |
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