Hydrophobic Interaction Chromatography (HIC) is often employed as a polishing step to remove aggregates for the purification of therapeutic proteins in the biopharmaceutical industry. To accelerate the process development and save the costs of performing time- and resource-intensive experiments, advanced model-based process design and optimization are necessary. Due to the unclear adsorption mechanism of the salt-dependent interaction between the protein and resin, the development of an accurate mechanistic model to describe the complex HIC behavior is challenging. In this work, an isotherm derived from Wang et al. is modified by adding three extra parameters together with an equilibrium dispersive model to represent the HIC process. To reduce the development effort of isotherm equations and extract missing information from the available data, a hybrid model is constructed by combining a simple and well-known multi-component Langmuir isotherm (MCL) with a neural network (NN). It is observed that the structure of the hybrid model is of critical importance to the accuracy of the developed model. During parameter estimation, a regularization strategy is incorporated to prevent overfitting. Furthermore, the impact of NN structures and regularization rates are comprehensively investigated. One of the interesting findings was that a simple NN with one hidden layer with two nodes and sigmoid as the activation function, significantly outperforms the mechanistic model, with a 62% improvement in accuracy in calibration and 31.4% in validation. To ensure the generalizability of the developed hybrid model, an in-silico dataset is generated using the mechanistic model to test the extrapolation capability of the hybrid model. Process optimization is also carried out to find the optimal operating conditions under product quality constraints using the developed hybrid model.

在生物制药行业，疏水相互作用色谱 (HIC) 通常用作纯化步骤以去除聚集体以纯化治疗性蛋白质。为了加速工艺开发并节省执行时间和资源密集型实验的成本，先进的基于模型的工艺设计和优化是必要的。由于蛋白质和树脂之间依赖于盐的相互作用的吸附机制尚不清楚，因此开发描述复杂 HIC 行为的准确机制模型具有挑战性。在这项工作中，来自 Wang 等人的等温线。通过添加三个额外参数以及平衡色散模型来表示 HIC 过程来修改。为了减少等温方程的开发工作并从可用数据中提取缺失的信息，通过将简单且众所周知的多组分朗缪尔等温线 (MCL) 与神经网络 (NN) 相结合来构建混合模型。据观察，混合模型的结构对于开发模型的准确性至关重要。在参数估计过程中，引入正则化策略以防止过度拟合。此外，还全面研究了神经网络结构和正则化率的影响。其中一项有趣的发现是，具有一个隐藏层、两个节点和 sigmoid 作为激活函数的简单神经网络明显优于机械模型，校准精度提高了 62%，验证精度提高了 31.4%。为了确保开发的混合模型的通用性，一个 据观察，混合模型的结构对于开发模型的准确性至关重要。在参数估计过程中，引入正则化策略以防止过度拟合。此外，还全面研究了神经网络结构和正则化率的影响。其中一项有趣的发现是，具有一个隐藏层、两个节点和 sigmoid 作为激活函数的简单神经网络明显优于机械模型，校准精度提高了 62%，验证精度提高了 31.4%。为了确保开发的混合模型的通用性，一个 据观察，混合模型的结构对于开发模型的准确性至关重要。在参数估计过程中，引入正则化策略以防止过度拟合。此外，还全面研究了神经网络结构和正则化率的影响。其中一项有趣的发现是，具有一个隐藏层、两个节点和 sigmoid 作为激活函数的简单神经网络明显优于机械模型，校准精度提高了 62%，验证精度提高了 31.4%。为了确保开发的混合模型的通用性，一个 全面研究了 NN 结构和正则化率的影响。其中一项有趣的发现是，具有一个隐藏层、两个节点和 sigmoid 作为激活函数的简单神经网络明显优于机械模型，校准精度提高了 62%，验证精度提高了 31.4%。为了确保开发的混合模型的通用性，一个 全面研究了 NN 结构和正则化率的影响。其中一项有趣的发现是，具有一个隐藏层、两个节点和 sigmoid 作为激活函数的简单神经网络明显优于机械模型，校准精度提高了 62%，验证精度提高了 31.4%。为了确保开发的混合模型的通用性，一个使用机械模型生成计算机数据集以测试混合模型的外推能力。还进行了工艺优化，以使用开发的混合模型在产品质量约束下找到最佳操作条件。