Accounting for erroneous model structures in biokinetic process models

In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. In this work, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design. © 2020 Elsevier LtdMarc B. Neumann acknowledges financial support provided by the Spanish Government through the BC3 María de Maeztu excellence accreditation 2018–2022 (MDM-2017-0714) and the Ramón y Cajal grant (RYC-2013-13628); and by the Basque Government through the BERC 2018-2021 program

Automated layer identification in large area additive manufacturing (LAAM): A comparison of image thresholding and edge detection techniques

Our study aims to develop an automated method for identifying layers on images of 3Dprinted walls from a LAAM printer, as manual identification is subjective and can be timeconsuming. We applied three different image processing methods to identify edges between layers: simple thresholding, Otsu thresholding, and Canny edge detection. Otsu thresholding was found to be the most accurate and required minimal manual intervention. From our study, we propose a new approach by going through essential steps for greater accuracy. This research demonstrates the feasibility of using computer-based methods to automatically identify layers in 3D printing, reducing manual time and effort and improving the strength and quality of 3D-printed parts.Mechanical Engineerin

Unfold principal component analysis and functional unfold principal component analysis for online plant stress detection

To be able to develop accurate plant-based irrigation scheduling tools, automatic and early detection of plant drought stress is of great importance. In this context, measurements of stem diameter variations are very promising as a source of information. These measurements are sensitive for drought stress, but also depend on changing microclimatic conditions. Specific data mining techniques, such as Unfold Principal Component Analysis (UPCA), have been developed to facilitate monitoring and diagnosing of such large-dimensional data sets. A UPCA model is used in this study to determine whether the measured stem diameter variations deviate from normal conditions due to drought stress. A newer technique, Functional Unfold Principal Component Analysis (FUPCA), combines functional data analysis with UPCA. The function parameters instead of the original data are then analysed by UPCA. The resulting FUPCA model is less complex and more robust compared to the original UPCA model. Moreover, FUPCA can handle days with missing data straightforwardly. The performances of UPCA and FUPCA models for online plant stress detection were investigated and compared to each other. Two pilot-scale setups were conducted: one with an herbaceous and one with a woody species. For both species, UPCA and FUPCA were shown to be applicable for stress detection. Both allowed successful detection days before visible symptoms appeared, while FUPCA exhibited a lesser parametric complexity

Multivariate nonlinear statistical process control of a sequencing batch reactor

This research describes the application of a multivariate statistical process control method to a pilot-scale sequencing batch reactor (SBR) using a hatchwise nonlinear monitoring technique for a denoising effect. Three-way batch data of normal batches are unfolded batch-wise and then a kernel principal component analysis (KPCA) is applied to capture the nonlinear dynamics within normal batch processes. The developed monitoring method was successfully applied to an 80-l sequencing batch reactor (SBR) for biological wastewater treatment, which is characterized by a variety of nonstationary and nonlinear characteristics. In the multivariate analysis and batch-wise monitoring, the developed nonlinear monitoring method can effectively capture the nonlinear relations within the batch process data and clearly showed the power of nonlinear process monitoring and denoising performance in comparison with linear methods.X116sciescopu

Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor

Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and soon. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearty; (2) multiple models with a;posterior probability for modeling different operating regions; (3) local batch monitoring by the T-2- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then; these local regions can be supervised separately; leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model. (c) 2006 Wiley Periodicals, Inc.X115557sciescopu

Transforming data into knowledge for improved wastewater treatment operation : A critical review of techniques

The aim of this paper is to describe the state-of-the art computer-based techniques for data analysis to improve operation of wastewater treatment plants. A comprehensive review of peer-reviewed papers shows that European researchers have led academic computer-based method development during the last two decades. The most cited techniques are artificial neural networks, principal component analysis, fuzzy logic, clustering, independent component analysis and partial least squares regression. Even though there has been progress on techniques related to the development of environmental decision support systems, knowledge discovery and management, the research sector is still far from delivering systems that smoothly integrate several types of knowledge and different methods of reasoning. Several limitations that currently prevent the application of computer-based techniques in practice are highlighted

Sensor validation and reconciliation for a partial nitrification process

Wastewater treatment plants (WWTP) are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function. In this paper, a structured residual approach with maximum sensitivity (SRAMS) based on the redundancy of the measurements is used to detect, identify and reconstruct single and multiple sensor faults in a single reactor for high activity ammonia removal over nitrite (SHARON) process. SRAMS is based on inferences, which are insensitive to the faults in the sensor of interest and sensitive to faults in the other sensors. It is used for four types of sensor failure detection: bias, drift, complete failure and precision degradation. The application of sensor validation shows that single and multiple sensor faults can be detected and that the fault magnitude and fault type can be estimated by the reconstruction scheme. This sensor validation method is not limited by the type or application of the considered sensors. The methodology can thus easily be applied for sensor surveillance of other continuously measuring sensors and analysers.X111415sciescopu