scholarly journals Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage

2021 ◽  
Vol 5 (6) ◽  
pp. 145
Author(s):  
Song-Jeng Huang ◽  
Matoke Peter Mose ◽  
Sathiyalingam Kannaiyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Energy Storage ◽  
Solid State ◽  
Hydrogen Storage ◽  
Low Cost ◽  
Operating Conditions ◽  
Material Behavior ◽  
Hydrogen Energy ◽  
Microstructure Modification

The use of Mg-based compounds in solid-state hydrogen energy storage has a very high prospect due to its high potential, low-cost, and ease of availability. Today, solid-state hydrogen storage science is concerned with understanding the material behavior of different compositions and structure when interacting with hydrogen. Finding a suitable material has remained an elusive idea, and therefore, this review summarizes works by various groups, the milestones they have achieved, and the roadmap to be taken on the study of hydrogen storage using low-cost magnesium composites. Mg-based compounds are further examined from the perspective of artificial intelligence studies, which helps to improve prediction of their properties and hydrogen storage performance. There exist several techniques to improve the performance of Mg-based compounds: microstructure modification, use of catalytic additives, and composition regulation. Microstructure modification is usually achieved by employing different synthetic techniques like severe plastic deformation, high energy ball milling, and cold rolling, among others. These synthetic approaches are discussed herein. In this review, a discussion of key parameters and operating conditions are highlighted in a view to finding high storage capacity and faster kinetics. Furthermore, recent approaches like machine learning have found application in guiding the experimental design. Hence, this review paper also explores how machine learning techniques have been utilized to fasten the materials research. It is however noted that this study is not exhaustive in itself.

Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence

2021 ◽  
Author(s):  
Ronald E. Vieira ◽  
Bohan Xu ◽  
Asad Nadeem ◽  
Ahmed Nadeem ◽  
Siamack A. Shirazi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Noise Level ◽  
Background Noise ◽  
Pipe Wall ◽  
Flow Patterns ◽  
Operating Conditions ◽  
Flow Conditions ◽  
Background Noise Level ◽  
Input Parameters

Abstract Solids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions. More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score. The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before. This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.

EFFICIENCY INVESTIGATION OF THE WAYS OF COMBINING NPP WITH A HYDROGEN COMPLEX FOR THE PURPOSE OF SALE OF HYDROGEN AND GENERATION OF ADDITIONAL ELECTRIC POWER

2020 ◽  
pp. 19-29
Author(s):  
V.E. Yurin ◽  
◽  
A.N. Egorov ◽  
D.O. Bashlykov ◽  
A.B. Moskalenko ◽  
...  
Keyword(s):  
Economic Efficiency ◽  
Low Cost ◽  
Energy System ◽  
Operating Conditions ◽  
Hydrogen Energy ◽  
Utilization Factor ◽  
Energy Complex ◽  
Load Schedule ◽  
Installed Capacity ◽  
Maximum Utilization

With an increase in the share of NPPs in the energy system, it becomes necessary for them to participate in the regulation of the electric load schedule. At the same time, the operation of the NPP with the maximum utilization factor of the installed capacity of the reactor was economically and technically justified. One of the promising ways to solve this problem is to install consumers-regulators at NPPs. The hydrogen energy complex can be effectively used as a consumer-regulator. The authors have previously developed an autonomous hydrogen energy generating complex, scientifically substantiated its economic efficiency. As the study has shown, the economic efficiency of an autonomous hydrogen energy complex directly depends on the sale tariffs for electricity. The low cost of electricity sold leads to a deterioration in economic indicators, up to a lack of recoupment. In this regard, as an alternative option, this work considers the possibility of selling hydrogen and oxygen as a commercial product at existing prices. A comparative study for a range of electricity tariffs and prices for hydrogen and oxygen was carried out on the basis of the methodology presented earlier by the authors, which makes it possible to study ways to improve NPPs on the basis of a comprehensive analysis of economic efficiency, safety and system effects achieved during the installation of new and modernization of existed equipment. The results obtained make it possible to choose the type of hydrogen energy complex depending on the operating conditions for the selected region of operation.

scholarly journals Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 62 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Frank R. Dunshea ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Food Industry ◽  
Emerging Technologies ◽  
Low Cost ◽  
Consumer Preference ◽  
Beverage Industry ◽  
Wide Range ◽  
Alcoholic Drinks ◽  
Different Levels

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

Conception and numerical simulation of heat and mass transfer in a solid state hydrogen storage reactor

2019 ◽  
Vol 87 (2) ◽  
pp. 20902 ◽  
Author(s):  
Amine Alaoui-Belghiti ◽  
Mourad Rkhis ◽  
Said Laasri ◽  
Abdelowahed Hajjaji ◽  
Mohamed Eljouad ◽  
...  
Keyword(s):  
Energy Storage ◽  
Solid State ◽  
Hydrogen Storage ◽  
Physical Phenomenon ◽  
Point Of View ◽  
Momentum Balance ◽  
Bed Temperature ◽  
Hydrogen Supply ◽  
Hydrogen Storage Performance ◽  
The Impact

Nowadays energy storage seems to be a vital point in any new energy paradigm. It has become an important and strategic issue, to ensure the energetic sufficiency of humanity. Indeed, hydrogen storage in solids has been proved and revealed as clean and efficient energy storage. Moreover, it can be thought as a seriously considered solution to enable renewable energy to be a part of our quotidian life. To achieve storing hydrogen in solid form, the present study aimed to concepts and simulates a solid-state hydrogen storage reactor (tank). An investigation of the parameters influencing the hydrogen storage performance is carried out. Meanwhile, to understand the physical phenomenon taking place during the storage of hydrogen, a 2D numerical modelling for a metal hydrides-based in hydrogen reactor is presented. A strong coupling between energy balance, kinetic law, as well as a mass momentum balance at sorbent bed temperature under a non-uniform pressure was resolved based on finite element method. The temporal evolutions of pressure, the raising temperature in the bed during the hydriding process as well as the impact of the hydrogen supply pressure within the tank are analysed and validated by comparison with the experimental work in literature, a good agreement is obtained. From an industrial point of view, this study can be used to design and manufacture an optimal solid-state hydrogen storage reactor.

scholarly journals Solid-State Hydrogen Storage: Materials, Systems and the Relevance of a Gender Perspective

2021 ◽  
Author(s):  
Erika Michela Dematteis ◽  
Jussara Barale ◽  
Marta Corno ◽  
Alessandro Sciullo ◽  
Marcello Baricco ◽  
...  
Keyword(s):  
Solid State ◽  
Hydrogen Storage ◽  
Operating Conditions ◽  
Hydrogen Sorption ◽  
Enthalpy Of Reaction ◽  
Gender Perspective ◽  
The Social ◽  
The Impact ◽  
Kinetics Of

This paper aims at addressing the exploitation of solid-state carriers for hydrogen storage, with attention paid both to the technical aspects, through a wide review of the available integrated systems, and to the social aspects, through a preliminary overview of the connected impacts from a gender perspective. As for the technical perspective, carriers to be used for solid-state hydrogen storage for various applications can be classified into two classes: metal and complex hydrides. Related crystal structures and corresponding hydrogen sorption properties are reviewed and discussed. Fundamentals of thermodynamics of hydrogen sorption evidences the key role of the enthalpy of reaction, which determines the operating conditions (i.e. temperatures and pressures). In addition, it rules the heat to be removed from the tank during hydrogen absorption and to be delivered to the tank during hydrogen desorption. Suitable values for the enthalpy of hydrogen sorption reaction for operating conditions close to ambient (i.e. room temperature and 1-10 bar of hydrogen) are close to 30 kJ·molH2 1. The kinetics of hydrogen sorption reaction is strongly related to the microstructure and to the morphology (i.e. loose powder or pellets) of the carriers. Usually, kinetics of hydrogen sorption reaction is rather fast, and the thermal management of the tank is the rate determining step of the processes. As for the social perspective, various scenarios for the applications in different socio-economic contexts of solid-state hydrogen storage technologies are described. As it occurs with the exploitation of other renewables innovative technologies, a wide consideration of the social factors connected to these processes is needed to assess the extent to which a specific innovation might produce positive or negative impacts in the recipient socio-economic system and to explore the potential role of the social components and dynamics in fostering the diffusion of the innovation itself. Attention has been addressed to the gender perspective, in view of the enhancement of hydrogen-related energy storage systems, intended both in terms of the role of women in triggering the exploitation of hydrogen-based storage as well as to the impact of this innovation in their current conditions, at work and in daily life.

scholarly journals Beer Aroma and Quality Traits Assessment Using Artificial Intelligence

Fermentation ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 56 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Near Infrared ◽  
Consumer Preferences ◽  
Low Cost ◽  
Physicochemical Characteristics ◽  
Decision Making Process ◽  
Quality Traits ◽  
Chemical Fingerprinting ◽  
Machine Learning Models

Increasing beer quality demands from consumers have put pressure on brewers to target specific steps within the beer-making process to modify beer styles and quality traits. However, this demands more robust methodologies to assess the final aroma profiles and physicochemical characteristics of beers. This research shows the construction of artificial intelligence (AI) models based on aroma profiles, chemometrics, and chemical fingerprinting using near-infrared spectroscopy (NIR) obtained from 20 commercial beers used as targets. Results showed that machine learning models obtained using NIR from beers as inputs were accurate and robust in the prediction of six important aromas for beer (Model 1; R = 0.91; b = 0.87) and chemometrics (Model 2; R = 0.93; b = 0.90). Additionally, two more accurate models were obtained from robotics (RoboBEER) to obtain the same aroma profiles (Model 3; R = 0.99; b = 1.00) and chemometrics (Model 4; R = 0.98; b = 1.00). Low-cost robotics and sensors coupled with computer vision and machine learning modeling could help brewers in the decision-making process to target specific consumer preferences and to secure higher consumer demands.

scholarly journals Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Fermentation ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 104
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Electronic Nose ◽  
Near Infrared ◽  
Raw Materials ◽  
Low Cost ◽  
Consumer Preference ◽  
Sensor Technology ◽  
Type Model ◽  
Data Prediction

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

Alaska’s Renewables-Source Fuel Energy Storage Pilot Plant: Toward Community Energy Independence via Solid State Ammonia Synthesis (SSAS)

2013 ◽  
Author(s):  
William C. Leighty
Keyword(s):  
Energy Storage ◽  
Solid State ◽  
Large Scale ◽  
Ammonia Synthesis ◽  
Low Cost ◽  
High Energy ◽  
High Energy Density ◽  
Energy Needs ◽  
Steel Tanks ◽  
Energy Independence

Alaska village survival is threatened by the high cost of imported fuels for heating, electricity generation, and vehicles. During Winter 2007–8, the price per gallon of heating oil and diesel generation fuel exceeded $8 in many villages. Many villagers were forced to move to Anchorage or Fairbanks. Although indigenous renewable energy (RE) resources may be adequate to supply a community’s total annual energy needs, the innate intermittent and seasonal output of the renewables — except geothermal, where available, which may be considered “baseload” — requires large-scale, low-cost energy storage to provide an annually-firm energy supply. Anhydrous ammonia, NH3, is the most attractive, carbon-free fuel for this purpose at Alaska village scale, because of its 17.8% mass hydrogen content and its high energy density as a low-pressure liquid, suitable for storage in inexpensive mild steel tanks. NH3 may be synthesized directly from renewable-source electricity, water, and atmospheric nitrogen (N2) via solid state ammonia synthesis (SSAS), a new process to be pioneered in Alaska.

scholarly journals Properties and Applications of Metal (M) dodecahydro-closo-dodecaborates (Mn=1,2B12H12) and Their Implications for Reversible Hydrogen Storage in the Borohydrides

Inorganics ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 106 ◽  
Author(s):  
Aiden Grahame ◽  
Kondo-François Aguey-Zinsou
Keyword(s):  
Solid State ◽  
Hydrogen Storage ◽  
Sustainable Energy ◽  
Energy Carrier ◽  
Hydrogen Energy ◽  
Storage Method ◽  
Modern Chemistry ◽  
Energy Economy ◽  
Hydrogen Technologies

Hydrogen has long been proposed as a versatile energy carrier that could facilitate a sustainable energy future. For an energy economy centred around hydrogen to function, a storage method is required that is optimised for both portable and stationary applications and is compatible with existing hydrogen technologies. Storage by chemisorption in borohydride species emerges as a promising option because of the advantages of solid-state storage and the unmatched hydrogen energy densities that borohydrides attain. One of the most nuanced challenges limiting the feasibility of borohydride hydrogen storage is the irreversibility of their hydrogen storage reactions. This irreversibility has been partially attributed to the formation of stable dodecahydro-closo-dodecaborates (Mn=1,2B12H12) during the desorption of hydrogen. These dodecaborates have an interesting set of properties that are problematic in the context of borohydride decomposition but suggest a variety of useful applications when considered independently. In this review, dodecaborates are explored within the borohydride thermolysis system and beyond to present a holistic discussion of the most important roles of the dodecaborates in modern chemistry.

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