scholarly journals Closed-loop optimization of fast-charging protocols for batteries with machine learning

Nature ◽  
2020 ◽  
Vol 578 (7795) ◽  
pp. 397-402 ◽  
Author(s):  
Peter M. Attia ◽  
Aditya Grover ◽  
Norman Jin ◽  
Kristen A. Severson ◽  
Todor M. Markov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Closed Loop ◽  
Loop Optimization ◽  
Fast Charging

scholarly journals Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning

2021 ◽  
Vol 16 ◽  
pp. 100296
Author(s):  
I. Ohkubo ◽  
Z. Hou ◽  
J.N. Lee ◽  
T. Aizawa ◽  
M. Lippmaa ◽  
...  
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Titanium Nitride ◽  
Closed Loop ◽  
Film Growth ◽  
Thin Film Growth ◽  
Loop Optimization

Application of a Combination of Machine Learning and Reservoir Physics to Increase Production in a Mature Waterflood Field in the San Jorge Basin in Argentina

2021 ◽  
Author(s):  
Javier Eusebio Gomez ◽  
Marcelo Robles ◽  
Cristian Di Giuseppe ◽  
Federico Galliano ◽  
Jeronimo Centineo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ensemble Kalman Filter ◽  
Pareto Front ◽  
Closed Loop ◽  
Optimization Techniques ◽  
Large Field ◽  
Initial Model ◽  
Initial Field ◽  
Loop Optimization ◽  
Novel Technology

Abstract This paper presents the process and results of the application of Data Physics to optimize production of a mature field in the Gulf of San Jorge Basin in Argentina. Data Physics is a novel technology that blends the reservoir physics (black oil) used in traditional numerical simulation with machine learning and advanced optimization techniques. Data Physics was described in detail in a prior paper (Sarma, et al SPE-185507-MS) as a physics-based modeling approach augmented by machine learning. In essence, historical production and injection data are assimilated using an Ensemble Kalman Filter (EnKF) to infer the petrophysical parameters and create a predictive model of the field. This model is then used with Evolutionary Algorithms (EA) to find the pareto front for multiple optimization objectives like production, injection and NPV. Ultimately, the main objective of Data Physics is to enable Closed Loop Optimization. The technology was applied on a small section of a very large field in the Gulf of San Jorge comprised of 134 wells including 83 active producers and 27 active water injectors; up to 12 mandrels per well are used to provide with selective injection, while production is carried out in a comingled manner. Production zonal allocation is calculated using an in-house process based on swabbing tests and recovery factors and is used as input to the Data Physics application, while injection allocation is based on tracer logs performed in each injection well twice a year. This paper describes the modeling and optimization phases as well as the implementation in the field and the results obtained after performing two close loop optimization cycles. The initial model was developed between October and December 2018 and initial field implementation took place between January to March 2019. A second optimization cycle was then executed in January 2020 and results observed for several months.

The Effect of Chemical Representation on Active Machine Learning Towards Closed-Loop Optimization

2022 ◽  
Author(s):  
Alexander Pomberger ◽  
Antonio Pedrina McCarthy ◽  
Ahmad Khan ◽  
Simon Sung ◽  
Connor Taylor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Reaction ◽  
Closed Loop ◽  
Reaction Yield ◽  
Catalytic Systems ◽  
Loop Optimization ◽  
Reaction Optimization ◽  
Initial Dataset ◽  
Dataset Size ◽  
The Impact

Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices. Closed-loop optimization featuring active Machine Learning (ML) represents a powerful strategy for automating reaction optimization. However, the translation of chemical reaction conditions into a machine-readable format comes with the challenge of finding highly informative features which accurately capture the factors for reaction success and allow the model to learn efficiently. Herein, we compare the efficacy of different calculated chemical descriptors for a high throughput generated dataset to determine the impact on a supervised ML model when predicting reaction yield. Then, the effect of featurization and size of the initial dataset within a closed-loop reaction optimization was examined. Finally, the balance between descriptor complexity and dataset size was considered. Ultimately, tailored descriptors did not outperform simple generic representations, however, a larger initial dataset accelerated reaction optimization.

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

scholarly journals Stability of Optimal Closed-Loop Cleaning Scheduling and Control with Application to Heat Exchanger Networks under Fouling

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1623
Author(s):  
Federico Lozano Santamaria ◽  
Sandro Macchietto
Keyword(s):  
Heat Exchanger ◽  
Closed Loop ◽  
Operating Cost ◽  
Optimization Procedure ◽  
Loop Model ◽  
Loop Scheduling ◽  
Loop Optimization ◽  
Trade Offs ◽  
Heat Exchanger Networks ◽  
And Control

Heat exchanger networks subject to fouling are an important example of dynamic systems where performance deteriorates over time. To mitigate fouling and recover performance, cleanings of the exchangers are scheduled and control actions applied. Because of inaccuracy in the models, as well as uncertainty and variability in the operations, both schedule and controls often have to be revised to improve operations or just to ensure feasibility. A closed-loop nonlinear model predictive control (NMPC) approach had been previously developed to simultaneously optimize the cleaning schedule and the flow distribution for refinery preheat trains under fouling, considering their variability. However, the closed-loop scheduling stability of the scheme has not been analyzed. For practical closed-loop (online) scheduling applications, a balance is usually desired between reactivity (ensuring a rapid response to changes in conditions) and stability (avoiding too many large or frequent schedule changes). In this paper, metrics to quantify closed-loop scheduling stability (e.g., changes in task allocation or starting time) are developed and then included in the online optimization procedure. Three alternative formulations to directly include stability considerations in the closed-loop optimization are proposed and applied to two case studies, an illustrative one and an industrial one based on a refinery preheat train. Results demonstrate the applicability of the stability metrics developed and the ability of the closed-loop optimization to exploit trade-offs between stability and performance. For the heat exchanger networks under fouling considered, it is shown that the approach proposed can improve closed-loop schedule stability without significantly compromising the operating cost. The approach presented offers the blueprint for a more general application to closed-loop, model-based optimization of scheduling and control in other processes.

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