Digital Chemical Engineering最新文献_第7页

Dextrosinistral reading of SMILES notation: Investigation into origin of non-sense code from string manipulations 对 SMILES 符号的 Dextrosinistral 阅读：调查字符串操作产生的无意义代码的起源

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-02-22 DOI: 10.1016/j.dche.2025.100222

Anup Paul

The SMILES notation provides a digital way to represent any chemical structure in the form of a string of ASCII characters, therefore, a preferred data medium for machine learning models. As Chomsky type-2 language, SMILES notation is supported with context-free grammar, raising errors for invalid string arrangements. Numerous efforts have been made to recover chemical structures in invalid SMILES strings. Exploring the flexibility of SMILES notations of real molecules would give critical information related to SMILES string reorganizations and sources of errors. Present study examined the potential for reading SMILES notation from right-to-left, known as dextrosinistral reading, and evaluated the effect of new character combinations on the representative chemical structures. The study developed a set of string operations to reverse the order of characters in the SMILES string while maintaining the context-free grammar of SMILES notation. These operations were tested on SMILES notation of over two hundred natural products, resulting in diverse changes at the chemical structure level, including reverting to the original structure, reconfiguring into an isomeric structure, or generating compounds having valency errors. The DFS-tree profiled the changes in chemical structures from reorganizations of SMILES strings and identified the source of atoms with valence errors. Molecular Mechanics (mm2) calculations showed that a group of newly generated chemical structures has total energy in a range of transition state molecular complexes. While the analyses of machine learning models showed the need for cheminformatics tools, such as RDKit and OpenBabel libraries, to develop modules that can fingerprint the reorganized SMILES strings containing atoms of explicit valences. The outcome of the present study highlighted the diversity and flexibility of SMILES notation, and may provide a new source of data required for developing the cheminformatics functionalities necessary to advance machine learning-based chemical discovery.

SMILES符号提供了一种以ASCII字符字符串形式表示任何化学结构的数字方式，因此是机器学习模型的首选数据介质。作为Chomsky type-2语言，SMILES表示法支持与上下文无关的语法，这会引发无效字符串排列的错误。为了恢复无效smile字符串中的化学结构，人们做了大量的努力。探索真实分子的SMILES符号的灵活性将提供与SMILES字符串重组和错误来源相关的关键信息。本研究考察了从右到左阅读smile符号的可能性，即右旋阅读，并评估了新字符组合对代表性化学结构的影响。该研究开发了一组字符串操作来反转SMILES字符串中的字符顺序，同时保持SMILES符号的上下文无关语法。这些操作在超过200种天然产物的SMILES符号上进行了测试，在化学结构水平上产生了不同的变化，包括恢复到原始结构，重新配置为同分异构体结构，或产生具有价错误的化合物。dfs树分析了smile链重组后化学结构的变化，并确定了价错原子的来源。分子力学（mm2）计算表明，一组新生成的化学结构的总能量处于过渡态分子复合物的范围内。而对机器学习模型的分析表明，需要化学信息学工具，如RDKit和OpenBabel库，来开发可以识别包含显价原子的重组SMILES字符串的模块。本研究的结果突出了SMILES符号的多样性和灵活性，并可能为开发基于机器学习的化学发现所需的化学信息学功能提供新的数据来源。

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引用次数: 0

Microwave drying of basil (Ocimum sanctum) leaves with chitosan coating pretreatment: Bibliometric analysis and optimization 壳聚糖包衣预处理罗勒叶微波干燥：文献计量学分析与优化

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-02-19 DOI: 10.1016/j.dche.2025.100225

Heri Septya Kusuma, Debora Engelien Christa Jaya, Nafisa Illiyanasafa, Endah Kurniasari, Kania Ludia Ikawati

This study optimized microwave drying of Ocimum sanctum (basil) leaves with chitosan coating pretreatment to improve drying efficiency and environmental impact. A bibliometric analysis revealed limited research on microwave-assisted drying methods combined with pretreatments. Using the Box-Behnken Design (BBD) within the Response Surface Methodology (RSM), the study evaluated the effects of drying time, microwave power, basil leaf mass, and chitosan concentration. Results showed that the optimum drying parameters were: drying time of 240 s, microwave power of 264.03 W, basil leaf mass of 14.36 g, and chitosan concentration of 1.39 %. Under these conditions, the moisture removal efficiency reached 61.6184 %, with relative energy consumption of 0.9698 kWh g^-1 and CO₂ emissions of 0.7758 kg g^-1. The findings demonstrate that microwave drying with chitosan coating reduces energy consumption and environmental emissions while maintaining product quality.

本研究优化了壳聚糖包衣预处理罗勒叶微波干燥工艺，提高了干燥效率和对环境的影响。文献计量学分析显示，微波辅助干燥方法与预处理相结合的研究有限。采用响应面法（RSM）中的Box-Behnken设计（BBD），研究了干燥时间、微波功率、罗勒叶质量和壳聚糖浓度的影响。结果表明，最佳干燥参数为：干燥时间240 s，微波功率264.03 W，罗勒叶质量14.36 g，壳聚糖浓度1.39%。在此条件下，除湿效率达到61.6184%，相对能耗为0.9698 kWh g-1， CO2排放量为0.7758 kg g-1。研究结果表明，壳聚糖涂层微波干燥在保持产品质量的同时，降低了能耗和环境排放。

引用次数: 0

Study on the Switching Model Predictive Control Algorithm in Batch Polymerization Process 间歇聚合过程中切换模型预测控制算法的研究

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-03-20 DOI: 10.1016/j.dche.2025.100232

Jong Nam Kim , Chun Bae Ma , Hyok Jo , Un Chol Han , Hyon-Tae Pak , Son Il Hong , Ri Myong Kim

In the batch polymerization process, temperature control is generally a challenging task. In this paper, a new switching model predictive control algorithm that can be effectively used for the temperature control of batch polymerization process is developed and its effectiveness is verified by introducing it to industrial batch polyvinyl chloride polymerization process. Firstly, a general analysis of the polymerization process is conducted, and based on this, the reaction starting point is determined. Secondly, a switching model identification method considering the reaction starting point and the reaction heat generated after the reaction starts is proposed. Finally, a switching model predictive control algorithm that determines the optimal manipulated value based on the on-line updated step response model is constructed, and a cascade control system using this algorithm is introduced to the temperature control of batch polyvinyl chloride suspension polymerization process. The results show that the proposed control system can significantly improve temperature control performance (overshoot: 0.2%, root mean square error: 0.3) compared to before introduction (overshoot: 1.1%, root mean square error: 1.2ྟC) .

在间歇聚合过程中，温度控制通常是一个具有挑战性的任务。本文提出了一种可有效用于间歇聚合过程温度控制的切换模型预测控制算法，并将其应用于工业间歇聚氯乙烯聚合过程中，验证了该算法的有效性。首先对聚合过程进行总体分析，在此基础上确定反应起始点。其次，提出了考虑反应起始点和反应开始后产生的反应热的切换模型辨识方法。最后，构建了基于在线更新阶跃响应模型确定最优操纵值的切换模型预测控制算法，并将该算法引入到间歇聚氯乙烯悬浮聚合过程的温度控制中。结果表明，与引入前（超调量：1.1%，均方根误差：1.2）相比，该控制系统能显著提高温度控制性能（超调量：0.2%，均方根误差：0.3）。

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引用次数: 0

A tutorial review of policy iteration methods in reinforcement learning for nonlinear optimal control 非线性最优控制强化学习中的策略迭代方法教程综述

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-03-27 DOI: 10.1016/j.dche.2025.100231

Yujia Wang , Xinji Zhu , Zhe Wu

Reinforcement learning (RL) has been a powerful framework for designing optimal controllers for nonlinear systems. This tutorial review provides a comprehensive exploration of RL techniques, with a particular focus on policy iteration methods for the development of optimal controllers. We discuss key theoretical aspects, including closed-loop stability and convergence analysis of learning algorithms. Additionally, the review addresses practical challenges encountered in real-world applications, such as the development of accurate process models, incorporating safety guarantees during learning, leveraging physics-informed machine learning and transfer learning techniques to overcome learning difficulties, managing model uncertainties, and enabling scalability through distributed RL. To demonstrate the effectiveness of these approaches, a simulation example of a chemical reactor is presented, with open-source code made available on GitHub. The review concludes with a discussion of open research questions and future directions in RL-based control of nonlinear systems.

强化学习（RL）已成为设计非线性系统最优控制器的有力框架。本教程综述提供了对强化学习技术的全面探索，特别关注用于开发最优控制器的策略迭代方法。我们讨论了关键的理论方面，包括闭环稳定性和收敛分析的学习算法。此外，该综述还解决了在实际应用中遇到的实际挑战，例如开发准确的过程模型，在学习过程中结合安全保证，利用物理信息的机器学习和迁移学习技术来克服学习困难，管理模型不确定性，并通过分布式强化学习实现可扩展性。为了演示这些方法的有效性，本文给出了一个化学反应器的模拟示例，并在GitHub上提供了开源代码。最后，讨论了基于rl的非线性系统控制的开放性研究问题和未来发展方向。

引用次数: 0

A degradation-related slow feature analysis for equipment health indicator extraction and remaining useful life prediction 用于设备健康指标提取和剩余使用寿命预测的退化相关慢特征分析

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-06-02 DOI: 10.1016/j.dche.2025.100243

Qilin Qu , Linhui Wang , I.-Yen Wu , David Shan-Hill Wong , Ying Zheng , Yuan Yao

Predicting the Remaining Useful Life (RUL) of equipments has recently become a crucial technology for assessing operational safety and assisting maintenance decision-making. Numerous studies have demonstrated that a low-dimensional Health Indicator (HI) can be constructed from multidimensional sensor readings related to degradation, and the prediction of RUL can be based on similarities of HI. However, existing approaches for HI construction ignore neither the slow and monotonic nature of a degradation feature nor correlations between HI and RUL. To address this issue, this paper proposes a degradation-related slow feature analysis (DRSFA) method for extracting HIs and applying them in RUL prediction. Specifically, an objective function and its corresponding closed-form solution are proposed, aiming at extracting a health indicator from multidimensional degradation parameters to represent the slow degradation trend of an equipment and is correlated with its RUL. In DRSFA, HIs of each segment of lifecycle data is extracted separately rather than by a unified model, thereby enhancing its scalability as new data become available. As an HI extractor, DRSFA can serve as a plug-and-play module for RUL prediction based on similarity matching. Finally, experiments conducted on the CMAPSS dataset for aero-engine RUL assessment from NASA validate that the proposed method effectively balances RUL prediction accuracy, interpretability, and scalability.

设备剩余使用寿命（RUL）预测已成为评估设备运行安全性和辅助维修决策的一项重要技术。大量研究表明，可以从与退化相关的多维传感器读数构建低维健康指标（HI），并且可以基于HI的相似性来预测RUL。然而，现有的HI构建方法既没有忽略退化特征的缓慢和单调性，也没有忽略HI与RUL之间的相关性。为了解决这一问题，本文提出了一种与退化相关的慢特征分析（DRSFA）方法来提取HIs并将其应用于RUL预测。具体而言，提出了一个目标函数及其对应的封闭解，旨在从多维退化参数中提取一个健康指标，以表示设备的缓慢退化趋势，并与设备的RUL相关。在DRSFA中，生命周期数据的每个片段的HIs是单独提取的，而不是由统一的模型提取，从而增强了新数据可用时的可扩展性。DRSFA作为一种HI提取器，可以作为基于相似性匹配的规则预测的即插即用模块。最后，在NASA航空发动机RUL评估的CMAPSS数据集上进行了实验，验证了该方法有效地平衡了RUL预测精度、可解释性和可扩展性。

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引用次数: 0

CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network 基于cfd的变涡长动态气旋GMDH人工神经网络优化

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-04-29 DOI: 10.1016/j.dche.2025.100241

Hamed Safikhani , Somayeh Davoodabadi Farahani , Lakhbir Singh Brar , Faroogh Esmaeili

Dynamic cyclone separators with adjustable vortex length are widely used in industrial applications such as particle collection and air pollution control. However, optimizing their performance remains a challenge due to complex fluid–particle interactions. This research introduces a three-step multi-objective optimization framework for dynamic cyclones with adjustable vortex length. Initially, computational fluid dynamics (CFD) simulations are utilized to examine airflow behavior in different cyclone designs. The Reynolds-averaged Navier-Stokes (RANS) equations, combined with the Reynolds stress turbulence model, are employed to model turbulence. The Eulerian-Lagrangian approach is used to track particle motion, while the Discrete Random Walk technique simulates velocity variations. In the second phase, data obtained from the numerical simulations is used to construct objective function models, focusing on minimizing pressure drop and maximizing collection efficiency. These models are developed using artificial neural networks based on the Group Method of Data Handling (GMDH). The final step involves optimizing the cyclone designs through the non-dominated sorting genetic algorithm (NSGA). The Pareto front is generated and analyzed, offering valuable insights into cyclone design improvements. The findings highlight that an optimized design for dynamic cyclones with variable vortex length can only be achieved through a systematic multi-objective optimization approach.

涡流长度可调的动态旋风分离器广泛应用于颗粒收集和空气污染控制等工业领域。然而，由于复杂的流体-颗粒相互作用，优化它们的性能仍然是一个挑战。提出了涡长可调动态气旋的三步多目标优化框架。首先，利用计算流体动力学（CFD）模拟来研究不同旋风分离器设计中的气流行为。采用Reynolds-average Navier-Stokes （RANS）方程，结合Reynolds应力湍流模型来模拟湍流。欧拉-拉格朗日方法用于跟踪粒子运动，而离散随机漫步技术模拟速度变化。第二阶段，利用数值模拟得到的数据构建目标函数模型，以压降最小和收集效率最大化为目标。这些模型是基于数据处理分组方法（GMDH）的人工神经网络建立的。最后一步是通过非支配排序遗传算法（NSGA）优化旋风分离器设计。生成和分析帕累托锋面，为旋风设计改进提供有价值的见解。研究结果表明，变涡长动态气旋的优化设计只能通过系统的多目标优化方法来实现。

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引用次数: 0

Surrogate-based flowsheet model maintenance for Digital Twins 基于代用流程图的数字孪生模型维护

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-03-12 DOI: 10.1016/j.dche.2025.100228

Balázs Palotai , Gábor Kis , János Abonyi , Ágnes Bárkányi

Digital Twins (DTs) are transforming industrial processes by providing virtual models that mirror physical systems, enabling real-time monitoring and optimization. A major challenge in DTs in process industry, is maintaining the accuracy of flowsheet simulation models due to changes like equipment degradation and operational shifts. This study proposes a novel surrogate-based approach for the automated calibration of these models, which reduces reliance on manual adjustments and adapts to changes in the physical system. This study leverages surrogate models and particle swarm optimization to incorporate modeling considerations and measurement uncertainties, thereby automating model calibration and reducing manual interventions. In a refinery case study, our approach reduced calibration time for the sour water stripper Hysys model by 80% while maintaining the desired accuracy. These results highlight the method’s potential to enhance flowsheet model accuracy in digital twin systems and to support more robust and adaptable DT applications.

数字孪生（dt）通过提供反映物理系统的虚拟模型，实现实时监控和优化，正在改变工业流程。在过程工业中，DTs面临的一个主要挑战是，由于设备退化和操作转换等变化，保持流程图仿真模型的准确性。本研究提出了一种新的基于代理的模型自动校准方法，减少了对人工调整的依赖，并适应了物理系统的变化。本研究利用替代模型和粒子群优化来结合建模考虑和测量不确定性，从而自动化模型校准并减少人工干预。在一个炼油厂的案例研究中，我们的方法在保持预期精度的同时，将酸水提提器Hysys模型的校准时间减少了80%。这些结果突出了该方法在提高数字孪生系统中的流程模型精度以及支持更健壮和适应性更强的DT应用方面的潜力。

引用次数: 0

Automation and control of an experimental protonic membrane steam methane reforming system 实验质子膜蒸汽甲烷重整系统的自动化与控制

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-05-14 DOI: 10.1016/j.dche.2025.100240

Dominic Peters , Xiaodong Cui , Yifei Wang , Christopher G. Donahue , Jake Stanley , Carlos G. Morales-Guio , Panagiotis D. Christofides

Nickel dispersion on doped barium-zirconate ceramics is a state-of-the-art material formulation used to fabricate proton conducting membranes that can reform methane at lower operational temperatures (600 to 800 °

C

). Although steady-state operational data have been reported for these ion-conducting ceramic reformers, transient datasets are uncommon and not readily available. Moreover, the automation of protonic membrane reformers is a major technical challenge for the commercialization of modular thermo-electrochemical hydrogen generators with highly nonlinear process dynamics. Here, a multi-input multi-output feedback control scheme has been designed from a relative gain array analysis of three process variables for an experimental 500 W (thermal and electrochemical power consumption) protonic membrane reforming system. Specifically, the proposed control architecture automatically calculates hydrogen separation rate setpoints while safely and effectively reaching hydrogen production rate setpoints and desired steam-to-carbon ratios. The control architecture also drives the system to 99.6% methane conversion at a current density of 0.564 ± 0.0125 A

\cdot

cm⁻² at 788 °

C

. Internal temperature fluctuations are mostly constrained to

\pm

6.00 °

C

\cdot

min⁻¹, which improves catalyst longevity when operating at hydrogen recovery rates exceeding 50%. Chief among these findings is an experimental demonstration of a control scenario that alters the hydrogen production rate setpoint every 150 min without sacrificing system-wide controllability. Integrator windup scenarios and counterproductive control actions are also avoided through rational controller design and proper controller tuning exercises. Industrial-scale applications of protonic membrane reformers may therefore be automated to control up to three process variables and have up to three additional control degrees of freedom for process intensification and optimization, making for well-governed, autonomous hydrogen generation units.

掺杂锆酸钡陶瓷上的镍分散体是一种最先进的材料配方，用于制造质子导电膜，可以在较低的操作温度（600至800°C）下重组甲烷。虽然已经报道了这些离子传导陶瓷转化炉的稳态运行数据，但瞬态数据集并不常见，也不容易获得。此外，质子膜重整器的自动化是具有高度非线性过程动力学的模块化热电化学氢发生器商业化的主要技术挑战。本文基于三个过程变量的相对增益阵列分析，设计了一种多输入多输出反馈控制方案，用于500w质子膜重整实验系统（热和电化学功耗）。具体来说，所提出的控制架构自动计算氢分离率设定值，同时安全有效地达到产氢率设定值和所需的蒸汽碳比。在788℃下，当电流密度为0.564±0.0125 a·cm−2时，该控制架构还可使系统的甲烷转化率达到99.6%。内部温度波动大多被限制在±6.00°C⋅min - 1，当氢回收率超过50%时，提高了催化剂的寿命。这些发现中最主要的是一个控制方案的实验演示，该方案每150分钟改变一次产氢率设定值，而不会牺牲系统范围的可控性。通过合理的控制器设计和适当的控制器调优练习，还可以避免积分器上发条场景和适得其反的控制动作。因此，质子膜重整器的工业规模应用可以自动化控制多达三个过程变量，并具有多达三个额外的控制自由度，以进行过程强化和优化，从而形成良好的管理，自主制氢装置。

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引用次数: 0

RESPONSE- a resilient framework to manage cyber-attacks on cyber-physical process systems RESPONSE——一个弹性框架，用于管理对网络物理过程系统的网络攻击

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-06-01 Epub Date: 2025-04-26 DOI: 10.1016/j.dche.2025.100238

Luyang Liu, Zaman Sajid, Costas Kravaris, Faisal Khan

This paper presents a novel framework – Resilient Process cONtrol SystEm (RESPONSE) - to address the critical challenge of cyberattacks on cyber-physical process systems (CPS). The RESPONSE emphasizes adaptability to existing systems, operational stability independent of detection mechanism reliability, and enhanced system continuity and recovery during and after cyber incidents. RESPONSE is built on the National Institute of Standards and Technology (NIST) cybersecurity recommendation by leveraging redundant control architecture, secure detection mechanism, and integral error manipulation to maintain safe operations under attack conditions. It has transformative potential for enhancing CPS security, reliability, and economic performance. A comparative analysis and case studies are presented to demonstrate the framework’s ability to mitigate cyber threats, minimize downtime, and ensure rapid recovery.

本文提出了一个新的框架-弹性过程控制系统(RESPONSE) -来解决网络攻击对网络物理过程系统（CPS）的关键挑战。RESPONSE强调对现有系统的适应性，独立于检测机制可靠性的运行稳定性，以及在网络事件期间和之后增强系统的连续性和恢复能力。RESPONSE基于美国国家标准与技术研究所（NIST）的网络安全建议，通过利用冗余控制架构、安全检测机制和集成错误操作来维持攻击条件下的安全操作。它具有增强CPS安全性、可靠性和经济性能的变革性潜力。通过对比分析和案例研究，展示了该框架在缓解网络威胁、减少停机时间和确保快速恢复方面的能力。

引用次数: 0

Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection 集成机器学习加速工业脱碳：流线型化学溶剂选择汉森溶解度参数的预测

IF 3 Q2 ENGINEERING, CHEMICAL

Digital Chemical Engineering

Pub Date : 2025-03-01 Epub Date: 2024-12-13 DOI: 10.1016/j.dche.2024.100207

Eslam G. Al-Sakkari , Ahmed Ragab , Mostafa Amer , Olumoye Ajao , Marzouk Benali , Daria C. Boffito , Hanane Dagdougui , Mouloud Amazouz

Several processes and strategies have been developed to promote the utilization of lignin and to facilitate its market adoption across a broad spectrum of applications within the expanding lignin bioeconomy. However, the inherent variability in lignin properties, resulting from diverse feedstock sources and varied recovery and downstream processing methods, remains a significant challenge. This highlights the critical need to investigate lignin's miscibility and reactivity with polymers and solvents, as most lignin valorization pathways involve mixing, blending, or solubilization. Accurate estimation of Hansen solubility parameters (HSP) is crucial for solvent selection in several fields such as polymer science, coatings, adhesives, lignin-based biorefineries and solvent-based carbon capture. Traditional methods for predicting HSP are time-consuming and involve complex experiments, especially in applications dealing with carbon dioxide and lignin solubility. This paper introduces a novel ensemble modeling methodology based on machine learning (ML) techniques for accurate HSP prediction using Simplified Molecular Input Line Entry System (SMILES) codes as entries. The methodology integrates different ML approaches, including deep and shallow learning, to enhance prediction accuracy. Decision fusion of individual ML models is achieved through a hybrid approach combining non-learnable and learnable methods, resulting in reduced errors and enhanced accuracy. The results highlight the effectiveness of the ensemble-based methodology, which achieved 99% accuracy in predicting dispersion solubility parameters, outperforming other individual ML techniques. The proposed generic methodology, from data preprocessing to decision fusion through diverse ML algorithms, can be applied to various chemical analytics beyond HSP prediction.

为了促进木质素的利用，并在不断扩大的木质素生物经济的广泛应用中促进其市场采用，已经制定了一些过程和策略。然而，由于不同的原料来源和不同的回收和下游加工方法，木质素性质的内在变异性仍然是一个重大的挑战。这突出了研究木质素与聚合物和溶剂的混溶性和反应性的迫切需要，因为大多数木质素的增值途径涉及混合、共混或增溶。汉森溶解度参数（HSP）的准确估计对于聚合物科学、涂料、粘合剂、木质素基生物炼制和溶剂基碳捕集等多个领域的溶剂选择至关重要。传统的热稳定性预测方法耗时且涉及复杂的实验，特别是在处理二氧化碳和木质素溶解度的应用中。本文介绍了一种基于机器学习（ML）技术的新型集成建模方法，该方法使用简化分子输入行输入系统（SMILES）代码作为条目进行准确的热热反应预测。该方法集成了不同的机器学习方法，包括深度和浅学习，以提高预测准确性。单个ML模型的决策融合是通过结合不可学习和可学习方法的混合方法实现的，从而减少了错误，提高了准确性。结果突出了基于集成的方法的有效性，该方法在预测分散溶解度参数方面达到了99%的准确率，优于其他单个ML技术。提出的通用方法，从数据预处理到通过各种ML算法的决策融合，可以应用于HSP预测之外的各种化学分析。

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引用次数: 0