This article demonstrates a parameter estimation technique for bioprocesses that utilizes measurement noise in experimental data to determine credible intervals on parameter estimates, with this information of potential use in prediction, robust control, and optimization. To determine these estimates, the work implements Bayesian inference using nested sampling, presenting an approach to develop neural network‐ (NN) based surrogate models. To address challenges associated with nonuniform sampling of experimental measurements, an NN structure is proposed. The resultant surrogate model is utilized within a Nested Sampling Algorithm that samples possible parameter values from the parameter space and uses the NN to calculate model output for use in the likelihood function based on the joint probability distribution of the noise of output variables. This method is illustrated against simulated data, then with experimental data from a Sartorius fed‐batch bioprocess. Results demonstrate the feasibility of the proposed technique to enable rapid parameter estimation for bioprocesses.

中文翻译：

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生物过程中的噪声感知参数估计：使用具有非均匀数据采样的神经网络代理模型


本文演示了一种生物过程的参数估计技术，该技术利用实验数据中的测量噪声来确定参数估计的可信区间，并将这些信息用于预测、稳健控制和优化。为了确定这些估计值，这项工作使用嵌套采样实施了贝叶斯推理，提出了一种开发基于神经网络 （NN） 的代理模型的方法。为了解决与实验测量的非均匀采样相关的挑战，提出了一种 NN 结构。生成的代理模型在嵌套采样算法中使用，该算法从参数空间采样可能的参数值，并使用 NN 根据输出变量噪声的联合概率分布计算模型输出，以便在似然函数中使用。该方法先用模拟数据进行说明，然后用赛多利斯补料分批生物工艺的实验数据进行说明。结果证明了所提出的技术能够实现生物过程快速参数估计的可行性。