Optimal Antibody Purification Strategies Using Data-Driven Models

This work addresses the multiscale optimization of the purification processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational flow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.     

Trajectory optimization and positioning control for batch process using learning control

Efficiency and accuracy are critical in the motion control of a batch process. This paper proposes a new intelligent motion control method for a batch process based on reinforcement learning (RL) and iterative learning control (ILC). The proposed learning-based motion control method enables the system to learn from its previous experience. The motion control method can be divided into two parts: (1) RL-based trajectory optimization and (2) ILC-based positioning control. Experiments were conducted to demonstrate the effectiveness of the proposed method. The results indicate that the proposed method not only reduces the process time effectively while ensuring system stability, but also achieves excellent positioning accuracy.     

基于数据驱动的控制器综合性能优化

为了提高过程回路的控制性能,提出一种基于数据驱动的控制器综合性能优化方法.首先,通过分析虚拟参考反馈校正方法与内模控制器设计过程的联系,确定数据驱动控制器校正所需参考模型的结构;然后,定义一种结合绝对误差积分和最小方差性能基准的综合性能指标,再基于该指标确定参考模型的未决参数;最后,利用控制系统闭环数据实现对控制器参数...     

Hybrid water treatment cost prediction model for raw water intake optimization

In order to reduce the total cost of a dual source drinking water treatment plant operation, a comprehensive hybrid prediction model was built to estimate the necessary chemicals dosage and pumping energy costs for alternative source selection scenarios. Correlations between the water quality parameters and the required treatment chemicals were estimated using historical data and the expected pH variations associated with each chemical addition, which was based on the Caldwell–Lawrence diagram. The pumping energy costs were also estimated using a data-driven approach that was based on historical plant data. The research has practical implications for water treatment operators seeking to minimize plant operational costs through selecting raw water intake volumes for their treatment plant based on multiple source options and offtake tower gate levels. Future research seeks to better link current and future water treatment dosage cost predictions directly to water quality measurements taken from vertical profiling systems.     

A KPI-based process monitoring and fault detection framework for large-scale processes

Large-scale processes, consisting of multiple interconnected subprocesses, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel representation of each subprocess, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods.     

Linking bottom-up intonation stylization to discourse structure

A new approach for intonation stylization that enables the extraction of an intonation representation from prosodically unlabeled data is introduced. This approach yields global and local intonation contour classes arising from a contour-based, parametric and superpositional intonation stylization. Based on findings about the linguistic interpretation of the contour classes derived from corpus statistics and perception experiments, we created simple prediction models for the partial generation of intonation contours from discourse structure defined by discourse segment boundaries and the information status of nouns within these segments. The predicted intonation contours were evaluated by human judgments of adequacy that yielded a high accordance.     

Improving partial mutual information-based input variable selection by consideration of boundary issues associated with bandwidth estimation

Input variable selection (IVS) is vital in the development of data-driven models. Among different IVS methods, partial mutual information (PMI) has shown significant promise, although its performance has been found to deteriorate for non-Gaussian and non-linear data. In this paper, the effectiveness of different approaches to improving PMI performance is investigated, focussing on boundary issues associated with bandwidth estimation. Boundary issues, associated with kernel-based density and residual computations within PMI, arise from the extension of symmetrical kernels beyond the feasible bounds of potential inputs, and result in an underestimation of kernel-based marginal and joint probability distribution functions in the PMI. In total, the effectiveness of 16 different approaches is tested on synthetically generated data and the results are used to develop preliminary guidelines for PMI IVS. By using the proposed guidelines, the correct inputs can be identified in 100% of trials, even if the data are highly non-linear or non-Gaussian.     

Data-driven design of robust fault detection system for wind turbines

In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model.     

Data-based analysis of energy system in papermaking process

Papermaking process is highly energy-intensive, complicated, and influenced by multiple unit processes, which plays an important role in the energy consumption, especially of thermal energy and electricity energy. An energy management system (EMS) was built to acquire the systematic, consistent, and accurate online information of the production process in a typical coated paperboard mill. The EMS first fulfilled the online energy information calculation from the process data acquisition and integration, second provided the massive historical data to find out the internal variation of production process for inferring decisions. In a case study, the online energy flow analysis and the estimation of specific energy consumption (SEC) were carried out. Based on data-driven techniques, three different operation conditions were recognized by adopting the kernel principal component analysis and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) when the basis weight was 220?g/m². The energy consumption was described in each cluster. Particular attention needed to be paid to LP steam in the high-speed production process with the change in environmental temperature and humidity in cluster 3. A novel indicator was proposed to appraise the level of energy consumption in different paper machine speeds. The benchmarking SEC was extracted from the massive historical data for the estimation of energy-saving potential. With the designed capacity of 300,000 ton/year, the potential economic benefits will be 20127 thousand RMB/year.     

Data-Driven Based Fault Prognosis for Industrial Systems: A Concise Overview

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial systems. According to the results of fault prognosis, the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance. With the increased complexity and the improved automation level of industrial systems, fault prognosis techniques have become more and more indispensable. Particularly, the data-driven based prognosis approaches, which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data, gain great attention from different industrial sectors. In this context, the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems. Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted. Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods. Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.