scholarly journals A critical review of statistical calibration/prediction models handling data inconsistency and model inadequacy

AIChE Journal ◽  
2017 ◽  
Vol 63 (10) ◽  
pp. 4642-4665 ◽  
Author(s):  
Pascal Pernot ◽  
Fabien Cailliez
Keyword(s):  
Critical Review ◽  
Prediction Models ◽  
Data Inconsistency ◽  
Model Inadequacy ◽  
Statistical Calibration

scholarly journals NMR Techniques and Prediction Models for the Analysis of Species Formed in CO2 Capture Processes with Amine-Based Sorbents: A Critical Review

2020 ◽  
Vol 8 (16) ◽  
pp. 6173-6193 ◽  
Author(s):  
Xiayi Eric Hu ◽  
Qian Yu ◽  
Francesco Barzagli ◽  
Chao’en Li ◽  
Maohong Fan ◽  
...  
Keyword(s):  
Co2 Capture ◽  
Critical Review ◽  
Prediction Models ◽  
Nmr Techniques

Using Statistical Calibration for Model Verification and Validation, Diagnosis of Model Inadequacy, and Improving Simulation Accuracy

2018 ◽  
Author(s):  
Kevin OFlaherty ◽  
Zachary Graves ◽  
Lie Xiong ◽  
Mark Andrews
Keyword(s):  
Random Noise ◽  
Model Verification ◽  
Simulation Accuracy ◽  
Minimization Methods ◽  
Model Verification And Validation ◽  
Model Inadequacy ◽  
Calibration Techniques ◽  
Multiple Metrics ◽  
Uncalibrated Model ◽  
Statistical Calibration

The paper presents an application of statistical calibration techniques to a bracket design fatigue model simulated in COMSOL Multiphysics®. The calibration will tune the bracket’s material properties and fatigue characteristics. For illustrative purposes, the test data used to calibrate the simulation model will be generated from the same simulation routine with the addition of an intentionally applied bias and random noise to simulate model form and physical testing errors. The accuracy and conclusions from the statistically calibrated model will be compared with the uncalibrated model as well as a model calibrated with conventional error minimization methods. Multiple metrics will be shown which can be used for model validation, including a discrepancy map which characterizes inadequacies in the simulation. The metrics used in the comparison will also include results from optimization, sensitivity analysis, and propagation of uncertainties motivated by manufacturing variations during bracket fabrication. The results will demonstrate the importance of calibrating a model before drawing design conclusions.

scholarly journals Suicide prediction models: a critical review of recent research with recommendations for the way forward

2019 ◽  
Vol 25 (1) ◽  
pp. 168-179 ◽  
Author(s):  
Ronald C. Kessler ◽  
Robert M. Bossarte ◽  
Alex Luedtke ◽  
Alan M. Zaslavsky ◽  
Jose R. Zubizarreta
Keyword(s):  
Critical Review ◽  
Prediction Models ◽  
Suicide Prediction ◽  
The Way

scholarly journals A Critical Review on Predicting Fouling in RO Desalination

2017 ◽  
Author(s):  
Alejandro Ruiz-García ◽  
Noemi Melián-Martel ◽  
Ignacio Nuez
Keyword(s):  
Membrane Fouling ◽  
Critical Review ◽  
Prediction Models ◽  
Full Scale ◽  
Operating Conditions ◽  
Negative Effect ◽  
Ro Membrane ◽  
Desalination Plants ◽  
Industry Professionals

RO membrane fouling is one of the main challenges that membrane manufactures, the scientific community and industry professionals have to deal with. The consequences of this inevitable phenomenon have a negative effect on the performance of the desalination system. Predicting fouling in RO systems is key to evaluating the long-term operating conditions and costs. Much research has been done on fouling indices, methods, techniques and prediction models to estimate the influence of fouling on the performance of RO systems. This paper offers a critical review evaluating the state of industry knowledge in the development of fouling indices and models in membrane systems for desalination in terms of use and applicability. Despite major efforts in this field, there are gaps in terms of effective methods and models for the estimation of fouling in full-scale RO desalination plants. In existing models applied to full-scale RO desalination plants, neither the spacer geometry of membranes nor the efficiency and frequency of chemical cleanings - which play an important role in the performance of this process - are considered.

scholarly journals Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 369
Author(s):  
K Manjunath ◽  
Suman Tewary ◽  
Neha Khatri ◽  
Kai Cheng
Keyword(s):  
Surface Roughness ◽  
Process Monitoring ◽  
Critical Review ◽  
Prediction Models ◽  
Diamond Turning ◽  
Working Environment ◽  
Surface Generation ◽  
Surface Finishing ◽  
Ultraprecision Machining ◽  
Technical Specifications

The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are met following the contemporary manufacturing paradigm, such as surface roughness, surface texture, and topographical requirements. Ultraprecision machining (UPM) requirements are quite common and essential for products and components with optical finishing, including larger and highly accurate mirrors, infrared optics, laser devices, varifocal lenses, and other freeform optics that can satisfy the technical specifications of precision optical components and devices without further post-polishing. Ultraprecision machining can provide high precision, complex components and devices with a nanometric level of surface finishing. Nevertheless, the process requires an in-depth and comprehensive understanding of the machining system, such as diamond turning with various input parameters, tool features that are able to alter the machining efficiency, the machine working environment and conditions, and even workpiece and tooling materials. The non-linear and complex nature of the UPM process poses a major challenge for the prediction of surface generation and finishing. Recent advances in Industry 4.0 and machine learning are providing an effective means for the optimization of process parameters, particularly through in-process monitoring and prediction while avoiding the conventional trial-and-error approach. This paper attempts to provide a comprehensive and critical review on state-of-the-art in-surfaces monitoring and prediction in UPM processes, as well as a discussion and exploration on the future research in the field through Artificial Intelligence (AI) and digital solutions for harnessing the practical UPM issues in the process, particularly in real-time. In the paper, the implementation and application perspectives are also presented, particularly focusing on future industrial-scale applications with the aid of advanced in-process monitoring and prediction models, algorithms, and digital-enabling technologies.

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