scholarly journals Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1560
Author(s):  
Martin F. Luna ◽  
Andrea M. Ochsner ◽  
Véronique Amstutz ◽  
Damian von Blarer ◽  
Michael Sokolov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Continuous Culture ◽  
Hybrid Model ◽  
First Principles ◽  
Operating Conditions ◽  
Model Description ◽  
Process Conditions ◽  
Actual Process ◽  
Limited Growth

Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture needs to be operated in the so-called dual nutrient limitation condition, where both the nitrogen and carbon sources are kept at very low concentrations. Mathematical models can greatly assist both design and operation of the bioprocess, but are challenged by the complexity of the system, in particular by the dual nutrient-limited growth phenomenon, where the cells undergo a metabolic shift that abruptly changes their behavior. Traditional, non-structured mechanistic models based on Monod uptake kinetics can be used to describe the bioreactor operation under specific process conditions. However, in the absence of a model description of the metabolic phenomena inside the cell, the extrapolation to a broader operation domain (e.g., different feeding concentrations and dilution rates) may present mismatches between the predictions and the actual process outcomes. Such detailed models may require almost perfect knowledge of the cell metabolism and omic-level measurements, hampering their development. On the other hand, purely data-driven models that learn correlations from experimental data do not require any prior knowledge of the process and are therefore unbiased and flexible. However, many more data are required for their development and their extrapolation ability is limited to conditions that are similar to the ones used for training. An attractive alternative is the combination of the extrapolation power of first principles knowledge with the flexibility of machine learning methods. This approach results in a hybrid model for the growth and uptake rates that can be used to predict the dynamic operation of the bioreactor. Here we develop a hybrid model to describe the continuous production of PHA by Pseudomonas putida GPo1 culture. After training, the model with experimental data gained under different dilution rates and medium compositions, we demonstrate how the model can describe the process in a wide range of operating conditions, including both single and dual nutrient-limited growth.

Hybrid model for fault detection and diagnosis in an industrial distillation column

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Julia Picabea ◽  
Mauricio Maestri ◽  
Miryan Cassanello ◽  
Gabriel Horowitz
Keyword(s):  
Fault Detection ◽  
Hybrid Model ◽  
First Principles ◽  
Distillation Column ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Equivalent Model ◽  
Wide Range ◽  
Fault Detection And Identification ◽  
State Library

AbstractThe present work describes a method of automatic fault detection and identification based on a hybrid model (HM): First Principles – Neural Network. The FPM can simulate a wide range of situations while the NN corrects the model output using information from the historical data of the process. Operating conditions corresponding to different types of faults were simulated with the HM and saved with their description in a process state library. To detect a fault, the online measured data was compared with that corresponding to the operation under normal conditions. If a significant deviation was detected, the current state was compared with all the states stored in the process state library and it was identified as the one at the shortest distance. The method was tested with real data from a methanol-water industrial distillation column. During the studied period of operation of the plant, two faults were identified and reported. The proposed method was able to identify such failures more effectively than an equivalent model of first principles. The results obtained show that the proposed method has a great potential to be used in the automatic diagnosis of faults in refining and petrochemical processes.

Hybrid Modeling of Modified Mathematical Model for Gas Phase Olefine in Fluidized Bed Catalyst Reactors Using Artificial Neural Networks

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Ahmmed Saadi Ibrehem
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Mass Transfer ◽  
Heat And Mass Transfer ◽  
Fluidized Bed ◽  
Hybrid Model ◽  
Operating Conditions ◽  
Transfer Coefficients ◽  
Mass And Heat Transfer ◽  
Artificial Neural

The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics makes the design of such reactors a challenging task to accomplish. The models of such a process rely heavily on predictive empirical correlations for mass and heat transfer coefficients. Unfortunately, published empirical based correlations have the common shortcoming of low-prediction efficiency compared with experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under industrial operating conditions. The hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. This modeling approach can be used as an alternative to conventional modeling methods.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

Machine-Learning-Assisted First-Principles Calculations of Strained InAs1−xSbx Alloys for Curved Focal-Plane Arrays

2021 ◽  
Vol 15 (6) ◽  
Author(s):  
Alexandros Kyrtsos ◽  
John Glennon ◽  
Andreu Glasmann ◽  
Mark R. O’Masta ◽  
Binh-Minh Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Focal Plane ◽  
Focal Plane Arrays ◽  
First Principles Calculations

scholarly journals Optimal Cleaning Cycle Scheduling under Uncertain Conditions: A Flexibility Analysis on Heat Exchanger Fouling

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 93
Author(s):  
Alessandro Di Pretoro ◽  
Francesco D’Iglio ◽  
Flavio Manenti
Keyword(s):  
Safety Issue ◽  
Probability Distributions ◽  
Operating Conditions ◽  
Economic Losses ◽  
Optimal Time ◽  
Model Description ◽  
Chemical Cleaning ◽  
Uncertain Variables ◽  
Cycle Scheduling ◽  
Cleaning Cycle

Fouling is a substantial economic, energy, and safety issue for all the process industry applications, heat transfer units in particular. Although this phenomenon can be mitigated, it cannot be avoided and proper cleaning cycle scheduling is the best way to deal with it. After thorough literature research about the most reliable fouling model description, cleaning procedures have been optimized by minimizing the Time Average Losses (TAL) under nominal operating conditions according to the well-established procedure. For this purpose, different cleaning actions, namely chemical and mechanical, have been accounted for. However, this procedure is strictly related to nominal operating conditions therefore perturbations, when present, could considerably compromise the process profitability due to unexpected shutdown or extraordinary maintenance operations. After a preliminary sensitivity analysis, the uncertain variables and the corresponding disturbance likelihood were estimated. Hence, cleaning cycles were rescheduled on the basis of a stochastic flexibility index for different probability distributions to show how the uncertainty characterization affects the optimal time and economic losses. A decisional algorithm was finally conceived in order to assess the best number of chemical cleaning cycles included in a cleaning supercycle. In conclusion, this study highlights how optimal scheduling is affected by external perturbations and provides an important tool to the decision-maker in order to make a more conscious design choice based on a robust multi-criteria optimization.

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

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.

A New Hybrid Model for RUL Prediction through Machine Learning

2021 ◽  
Author(s):  
Zahra Esfahani ◽  
Karim Salahshoor ◽  
Behnam Farsi ◽  
Ursula Eicker
Keyword(s):  
Machine Learning ◽  
Hybrid Model

First principles and machine learning based superior catalytic activities and selectivities for N2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts

2021 ◽  
Author(s):  
Mohammad Zafari ◽  
Arun S. Nissimagoudar ◽  
Muhammad Umer ◽  
Geunsik Lee ◽  
Kwang S. Kim
Keyword(s):  
Machine Learning ◽  
Nitrogen Fixation ◽  
First Principles ◽  
Conjugated Polymer ◽  
2D Materials ◽  
Single Atom ◽  
Vacancy Defects ◽  
Catalytic Activities ◽  
Polymer Supported ◽  
New Machine

The catalytic activity and selectivity can be improved for nitrogen fixation by using hollow sites and vacancy defects in 2D materials, while a new machine learning descriptor accelerates screening of efficient electrocatalysts.

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