Manheim Derek C, Detwiler Russell L
Department of Civil and Environmental Engineering, University of California Irvine, United States.
MethodsX. 2019 Jun 7;6:1398-1414. doi: 10.1016/j.mex.2019.05.035. eCollection 2019.
Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed into a routine challenge in the field of Environmental Engineering. Among the main issues identified with parameter estimation, the model-data calibration approach is a crucial, yet an often overlooked and difficult optimization problem. Here, a novel and rigorous global, multi objective, and fully Bayesian optimization approach that overcomes challenges associated with multi-variate, sparse and noisy data, as well as highly non-linear model structures commonly encountered in Environmental Engineering practice is presented. This optimization approach allows an improved definition and targeting of the compromise solution space for all multivariate problems, allowing efficient convergence, and a Bayesian component to thoroughly explore parameter and model prediction uncertainty. This global optimization approach outperformed, in terms of parameter accuracy and precision, standard, local non-linear regression routines and overcomes issues associated with premature convergence and addresses overfitting of different variables in the calibration process. •A sequential single, multi-objective, and Bayesian optimization workflow was developed to accurately and reliably estimate unstructured kinetic model parameters.•The global, single objective approach defines the global optimum (the best compromise solution) and "extreme" parameter solutions for each variable, while the global, multi-objective approach confirms the "best" compromise solution space for the Bayesian search to target and convergence is assessed using the single objective results.•The Approximate Bayesian Computational approach fully explores parameter and model prediction uncertainty targeting the compromise solution space previously identified.
从非结构化动力学模型准确可靠地预测细菌生长和代谢对于工程生物处理和修复系统的正常运行和设计至关重要。因此，参数估计已成为环境工程领域的一项常规挑战。在参数估计所确定的主要问题中，模型-数据校准方法是一个关键但常被忽视且困难的优化问题。在此，提出了一种新颖且严格的全局、多目标和全贝叶斯优化方法，该方法克服了与多变量、稀疏和噪声数据以及环境工程实践中常见的高度非线性模型结构相关的挑战。这种优化方法允许对所有多变量问题的折衷解空间进行改进的定义和定位，实现高效收敛，并且贝叶斯组件能够全面探索参数和模型预测的不确定性。就参数的准确性和精度而言，这种全局优化方法优于标准的局部非线性回归程序，克服了与过早收敛相关的问题，并解决了校准过程中不同变量的过拟合问题。
• 开发了一种顺序单目标、多目标和贝叶斯优化工作流程，以准确可靠地估计非结构化动力学模型参数。
• 全局单目标方法为每个变量定义全局最优解（最佳折衷解）和“极端”参数解，而全局多目标方法确定贝叶斯搜索的“最佳”折衷解空间，并使用单目标结果评估收敛情况。
• 近似贝叶斯计算方法全面探索参数和模型预测的不确定性，以先前确定的折衷解空间为目标。
