scholarly journals From Black Box to Machine Learning: A Journey through Membrane Process Modelling

Membranes ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 574
Author(s):  
Claudia F. Galinha ◽  
João G. Crespo
Keyword(s):  
Machine Learning ◽  
Process Modelling ◽  
Model Membrane ◽  
Point Of View ◽  
Personal Perspective ◽  
Process Conditions ◽  
Monitoring And Control ◽  
Membrane Processes ◽  
Mechanistic Modelling ◽  
Process Monitoring And Control

Membrane processes are complex systems, often comprising several physicochemical phenomena, as well as biological reactions, depending on the systems studied. Therefore, process modelling is a requirement to simulate (and predict) process and membrane performance, to infer about optimal process conditions, to assess fouling development, and ultimately, for process monitoring and control. Despite the actual dissemination of terms such as Machine Learning, the use of such computational tools to model membrane processes was regarded by many in the past as not useful from a scientific point-of-view, not contributing to the understanding of the phenomena involved. Despite the controversy, in the last 25 years, data driven, non-mechanistic modelling is being applied to describe different membrane processes and in the development of new modelling and monitoring approaches. Thus, this work aims at providing a personal perspective of the use of non-mechanistic modelling in membrane processes, reviewing the evolution supported in our own experience, gained as research group working in the field of membrane processes. Additionally, some guidelines are provided for the application of advanced mathematical tools to model membrane processes.

scholarly journals The Use of Machine Tool Internal Encoders as Sensors in a Process Monitoring System

2013 ◽  
Vol 7 (4) ◽  
pp. 410-417 ◽  
Author(s):  
Tomas Beno ◽  
◽  
Jari Repo ◽  
Lars Pejryd ◽  
Keyword(s):  
Tool Wear ◽  
Machine Tool ◽  
Process Monitoring ◽  
Low Frequency ◽  
Machining Process ◽  
Process Conditions ◽  
Monitoring And Control ◽  
Signal Features ◽  
Process Monitoring And Control ◽  
Cutting Force Components

Tool wear in machining changes the geometry of the cutting edges, which affects the direction and amplitudes of the cutting force components and the dynamics in the machining process. These changes in the forces and dynamics are picked up by the internal encoders and thus can be used for monitoring of changes in process conditions. This paper presents an approach for the monitoring of amulti-toothmilling process. The method is based on the direct measurement of the output from the position encoders available in the machine tool and the application of advanced signal analysis methods. The paper investigates repeatability of the developed method and discusses how to implement this in a process monitoring and control system. The results of this work show that various signal features which are correlated with tool wear can be extracted from the first few oscillating components, representing the low-frequency components, of the machine axes velocity signatures. The responses from the position encoders exhibit good repeatability, especially short term repeatability while the long-term repeatability is more unreliable.

scholarly journals In-Line Observation of Laser Cladding Processes via Atomic Emission Spectroscopy

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4401
Author(s):  
Malte Schmidt ◽  
Philipp Huke ◽  
Christoph Gerhard ◽  
Knut Partes
Keyword(s):  
Laser Beam ◽  
Emission Spectroscopy ◽  
Base Material ◽  
Optical Emission ◽  
Type A ◽  
Atomic Emission ◽  
Process Conditions ◽  
Monitoring And Control ◽  
Melt Pool ◽  
Process Monitoring And Control

Direct metal deposition (DMD) can be used for the cladding of surfaces as well as repairing and additive manufacturing of parts and features. Process monitoring and control methods ensure a consistent quality during manufacturing. Monitoring by optical emission spectroscopy of the process radiation can provide information on process conditions and the deposition layer. The object of this work is to measure optical emissions from the process using a spectrometer and identify element lines within the spectra. Single spectra have been recorded from the process. Single tracks of Co-based powder (MetcoClad21) were clad on an S235 base material. The influence of varying process parameters on the incidence and intensity of element lines has been investigated. Moreover, the interactions between the laser beam, powder jet, and substrate with regard to spectral emissions have been examined individually. The results showed that element lines do not occur regularly. Therefore, single spectra are sorted into spectra including element lines (type A) and those not including element lines (type B). Furthermore, only non-ionised elements could be detected, with chromium appearing frequently. It was shown that increasing the laser power increases the incidence of type A spectra and the intensity of specific Cr I lines. Moreover, element lines only occurred frequently during the interaction of the laser beam with the melt pool of the deposition layer.

The Start-Up, Operation and Monitoring of High-Rate Anaerobic Treatment Systems: Discusser's Report

1991 ◽  
Vol 24 (8) ◽  
pp. 257-277 ◽  
Author(s):  
P. Weiland ◽  
A. Rozzi
Keyword(s):  
Process Control ◽  
Warning Signal ◽  
Anaerobic Treatment ◽  
High Rate ◽  
Process Conditions ◽  
Physical Parameters ◽  
Monitoring And Control ◽  
Start Up ◽  
Process Monitoring And Control ◽  
And Control

The reduction of the duration of start-up and the improvement of process control are important factors in order to increase the competitiveness of anaerobic high-rate reactor systems. This paper discusses and reviews the specific similarities and differences of UASB, filter and expanded/fluidized bed reactors with respect to start-up, operation, parameter monitoring and process control. The influence of microbial, biochemical and physical parameters upon reactor start-up and process performance is evaluated and methods for process monitoring and control are described. The different role of stability indicators, which give an early warning signal of oncoming unstable process conditions, and control variables, which must be kept constant during operation, is discussed with respect to process control and reactor start-up. The merits and weak points of each reactor system are presented and all systems are qualitatively compared.

DEVELOPMENT OF COMPUTERIZED MONITORING AND CONTROL SYSTEM OF THE MOBILE ROBOT’S POSITIONING ON LARGE FERROMAGNETIC SURFACES BASED ON INTELLIGENT TECHNIQUES. COMPLEX OF TASKS OF MONITORING AND AUTOMATIC CONTROL OF MOBILE

2019 ◽  
pp. 37-47
Author(s):  
Yao Yueqin ◽  
Oleksiy Kozlov ◽  
Oleksandr Gerasin ◽  
Galyna Kondratenko
Keyword(s):  
Automatic Control ◽  
Point Of View ◽  
Monitoring And Control ◽  
Large Area ◽  
Monitoring And Control System ◽  
Technical Parameters ◽  
Automatic Control Systems ◽  
Current State ◽  
Computerized Monitoring ◽  
And Control

Analysis and formalization of the monitoring and automatic control tasks of the MR for the movement and execution of various types of technological operations on inclined and vertical ferromagnetic surfaces are obtained. Generalized structure of mobile robotic complex is shown with main subsystems consideration. Critical analysis of the current state of the problem of development of universal structures of mobile robots (MRs) for the various types of technological operations execution and elaborations of computerized systems for monitoring and control of MR movement is done. In particular, wheeled, walked and crawler type MRs with pneumatic, vacuum-propeller, magnetic and magnetically operated clamping devices to grip with vertical and ceiling surfaces are reviewed. The constructive features of the crawler MR with magnetic clamping devices capable of moving along sloping ferromagnetic surfaces are considered. The basic technical parameters of the MR are shown for the further synthesis of computerized monitoring and automatic control systems. Formalization of the tasks of monitoring and control of the MR positioning at the processing of large area ferromagnetic surfaces is considered from the point of view of control theory.

scholarly journals Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

2021 ◽  
Author(s):  
Kjell Jorner ◽  
Tore Brinck ◽  
Per-Ola Norrby ◽  
David Buttar
Keyword(s):  
Machine Learning ◽  
Activation Energies ◽  
Accurate Prediction ◽  
Nucleophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Mechanistic Modelling

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

scholarly journals Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1241
Author(s):  
Véronique Gomes ◽  
Marco S. Reis ◽  
Francisco Rovira-Más ◽  
Ana Mendes-Ferreira ◽  
Pedro Melo-Pinto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Sugar Content ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Port Wine ◽  
Monitoring And Control ◽  
High Quality ◽  
Harvesting Stage

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

Study Progress on Grinding Surface Residual Stresses for Nano-Ceramic Coatings

2013 ◽  
Vol 753-755 ◽  
pp. 277-280 ◽  
Author(s):  
Wei Xiang Liu
Keyword(s):  
Residual Stress ◽  
High Hardness ◽  
Ceramic Materials ◽  
Ceramic Coatings ◽  
Grinding Process ◽  
Monitoring And Control ◽  
Surface Residual Stress ◽  
Surface Residual Stresses ◽  
Process Monitoring And Control ◽  
Grinding Mechanism

Nano-ceramic materials had high hardness and wear resistance. Combined with current technology and cost saving, nanostructured coatings technology were carried out, using HVOF ( high velocity oxygen fuel) or plasma spraying technique can obtain high quality ceramic coating on metal substrate. Ceramic coatings produced cracks in the grinding due to grinding surface residual stress. the coatings grinding surface residual stress of engineering ceramics have been researched, grinding surface residual stress in the nanostructured ceramic coatings are being researched. the researches in this field include grinding process modeling, abrasives and grinding parameters, grinding process monitoring and control and realization of the software, the grinding mechanism and grinding damage on the surface, grinding force prediction, on-line detection, grinding on nanocoating material is a multivariable complex process.

scholarly journals A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and Industry 4.0 applications

2021 ◽  
Author(s):  
Mert Gülçür ◽  
Ben Whiteside
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Injection Moulding ◽  
Industry 4.0 ◽  
Supervised Machine Learning ◽  
Process Conditions ◽  
Learning Models ◽  
Micro Injection Moulding ◽  
Machine Learning Models

AbstractThis paper discusses micromanufacturing process quality proxies called “process fingerprints” in micro-injection moulding for establishing in-line quality assurance and machine learning models for Industry 4.0 applications. Process fingerprints that we present in this study are purely physical proxies of the product quality and need tangible rationale regarding their selection criteria such as sensitivity, cost-effectiveness, and robustness. Proposed methods and selection reasons for process fingerprints are also justified by analysing the temporally collected data with respect to the microreplication efficiency. Extracted process fingerprints were also used in a multiple linear regression scenario where they bring actionable insights for creating traceable and cost-effective supervised machine learning models in challenging micro-injection moulding environments. Multiple linear regression model demonstrated %84 accuracy in predicting the quality of the process, which is significant as far as the extreme process conditions and product features are concerned.

Design and Implementation of Construction Materials Quality Detection Management System Based on Visual C++ and Database

2013 ◽  
Vol 433-435 ◽  
pp. 1635-1638
Author(s):  
Zhi Gang Feng ◽  
Fang Yuan Dai
Keyword(s):  
Information Management ◽  
Management System ◽  
Building Materials ◽  
Construction Materials ◽  
Information Management System ◽  
Sql Server ◽  
Monitoring And Control ◽  
Sql Server 2000 ◽  
Quality Detection ◽  
Process Monitoring And Control

In order to improve the efficiency of the traditional building materials information management, a construction materials quality detection management system is designed and developed. It includes three subsystems, they are quality testing system, information management system and process monitoring and control system. In the system, SQL Server 2000 is used as background database, Visual C++ 6.0 is used as the development platform, ADO is used as the connection between Visual C++ and SQL Server 2000. By using this system, the original paper records are stored in the database, the testing process is standardizes. The modernization of building materials information management is improved. This system has been successfully used in certain Building Materials Quality Supervision Station, which proved that the development process is suitable for Building Materials Supervision Station to quickly establish construction materials quality detection management system.

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