scholarly journals Decision making based on hybrid modeling approach applied to cellulose acetate based historical films conservation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abeer Al Mohtar ◽  
Moisés L. Pinto ◽  
Artur Neves ◽  
Sofia Nunes ◽  
Daniele Zappi ◽  
...  
Keyword(s):  
Cellulose Acetate ◽  
Hybrid Model ◽  
Prediction Models ◽  
Mechanistic Model ◽  
Hybrid Modeling ◽  
Conservation Strategies ◽  
Specific Information ◽  
Modeling Approach ◽  
Film Type ◽  
Historical Films

AbstractPreserving culture heritage cellulose acetate-based historical films is a challenge due to the long-term instability of these complex materials and a lack of prediction models that can guide conservation strategies for each particular film. In this work, a cellulose acetate degradation model is proposed as the basis for the selection of appropriate strategies for storage and conservation for each specimen, considering its specific information. Due to the formulation complexity and diversity of cellulose acetate-based films produced over the decades, we hereby propose a hybrid modeling approach to describe the films degradation process. The problem is addressed by a hybrid model that uses as a backbone a first-principles based model to describe the degradation kinetics of the pure cellulose diacetate polymer. The mechanistic model was successfully adapted to fit experimental data from accelerated aging of plasticized films. The hybrid model considers then the specificity of each historical film via the development of two chemometric models. These models resource on gas release data, namely acetic acid, and descriptors of the films (manufacturing date, AD-strip value and film type) to assess the current polymer degradation state and estimate the increase in the degradation rate. These estimations are then conjugated with storage conditions (e.g., temperature and relative humidity, presence of adsorbent in the film’s box) and used to feed the mechanistic model to provide the required time degradation simulations. The developed chemometric models provided predictions with accuracy more than 87%. We have found that the storage archive as well as the manufacturing company are not determining factors for conservation but rather the manufacturing date, off gas data as well as the film type. In summary, this hybrid modeling was able to develop a practical tool for conservators to assess films conservation state and to design storage and conservation policies that are best suited for each cultural heritage film.

Modeling the response of non-perennial streams to climate change impact: The Bouregreg watershed in Morocco

2021 ◽  
Author(s):  
Anna Maria De Girolamo ◽  
Youssef Brouziyne ◽  
Lahcen Benaabidate ◽  
Aziz Aboubdillah ◽  
Ali El Bilali ◽  
...  
Keyword(s):  
Climate Change ◽  
Assessment Tool ◽  
Swat Model ◽  
Circulation Model ◽  
Hybrid Modeling ◽  
Climate Data ◽  
Global Circulation Model ◽  
Stream Network ◽  
Modeling Framework ◽  
Modeling Approach

<p>The non-perennial streams and rivers are predominant in the Mediterranean region and play an important ecological role in the ecosystem diversity in this region. This class of streams is particularly vulnerable to climate change effects that are expected to amplify further under most climatic projections. Understanding the potential response of the hydrologic regime attributes to climatic stress helps in planning better conservation and management strategies. Bouregreg watershed (BW) in Morocco, is a strategic watershed for the region with a developed non-perennial stream network, and with typical assets and challenges of most Mediterranean watersheds. In this study, a hybrid modeling approach, based on the Soil and Water Assessment Tool (SWAT) model and Indicator of Hydrologic Alteration (IHA) program, was used to simulate the response of BW's stream network to climate change during the period: 2035-2050. Downscaled daily climate data from the global circulation model CNRM-CM5 were used to force the hybrid modeling framework over the study area. Results showed that, under the changing climate, the magnitude of the alteration will be different across the stream network; however, almost the entire flow regime attributes will be affected. Under the RCP8.5 scenario, the average number of zero-flow days will rise up from 3 to 17.5 days per year in some streams, the timing of the maximum flow was calculated to occur earlier by 17 days than in baseline, and the timing of the minimal flow should occur later by 170 days in some streams. The used modeling approach in this study contributed in identifying the most vulnerable streams in the BW to climate change for potential prioritization in conservation plans.</p>

scholarly journals A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

2020 ◽  
Vol 12 (11) ◽  
pp. 4730 ◽  
Author(s):  
Ping Wang ◽  
Hongyinping Feng ◽  
Guisheng Zhang ◽  
Daizong Yu
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Hybrid Model ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Input Vector ◽  
Period Function ◽  
Data Set ◽  
Luenberger Observer ◽  
Extraction Algorithm

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

scholarly journals A Hybrid Modeling Approach for Parking Assignment in Urban Areas

2020 ◽  
Author(s):  
Hanae Errousso ◽  
Jihane El Ouadi ◽  
El Arbi Abdellaoui Alaoui ◽  
Siham Benhadou ◽  
Hicham Medromi
Keyword(s):  
Urban Areas ◽  
Hybrid Modeling ◽  
Modeling Approach

Hybrid Modeling Approach for Melt Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Tesfaye Moges ◽  
Zhuo Yang ◽  
Kevontrez Jones ◽  
Shaw Feng ◽  
Paul Witherell ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Hybrid Model ◽  
Hybrid Models ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Powder Bed Fusion ◽  
Cfd Model ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

Abstract Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process-structure-property-performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser powder bed fusion (L-PBF) processes. Our unbiased, model integration method combines physics-based data and measurement data for approaching more accurate prediction of melt-pool width. Both a high-fidelity computational fluid dynamics (CFD) model and experiments utilizing optical images are used to generate a combined dataset of melt-pool widths. From this aggregated dataset, a hybrid model is developed using data-driven modeling techniques, including polynomial regression and Kriging methods. The performance of the hybrid model is evaluated by computing the average relative error and comparing it with the results of the simulations and surrogate models constructed from the original CFD model and experimental measurements. It is found that the proposed hybrid model performs better in terms of prediction accuracy and computational time. Future work includes a conceptual introduction on the use of an AM ontology to support improved model and data selection when constructing hybrid models. This study can be viewed as a significant step towards the use of hybrid models as predictive models with improved accuracy and without the sacrifice of speed.

A hybrid modeling approach for vibroacoustic systems with attached sound packages

2013 ◽  
Vol 133 (5) ◽  
pp. 3241-3241
Author(s):  
Luca Alimonti ◽  
Noureddine Atalla ◽  
Alain Berry ◽  
Franck Sgard
Keyword(s):  
Hybrid Modeling ◽  
Modeling Approach

Artificial neural network based hybrid modeling approach for flood inundation modeling

2021 ◽  
Vol 592 ◽  
pp. 125605
Author(s):  
Shuai Xie ◽  
Wenyan Wu ◽  
Sebastian Mooser ◽  
Q.J. Wang ◽  
Rory Nathan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Modeling ◽  
Flood Inundation ◽  
Modeling Approach ◽  
Artificial Neural ◽  
Flood Inundation Modeling ◽  
Inundation Modeling
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