scholarly journals An Artificial Intelligence Empowered Cyber Physical Ecosystem for Energy Efficiency and Occupation Health and Safety

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4214
Author(s):  
Petros Koutroumpinas ◽  
Yu Zhang ◽  
Steve Wallis ◽  
Elizabeth Chang
Keyword(s):  
Artificial Intelligence ◽  
Energy Efficiency ◽  
Mobile Networks ◽  
Energy Cost ◽  
Health And Safety ◽  
Field Tests ◽  
Energy Usage ◽  
Environment Friendly ◽  
Industrial Plants ◽  
Social Reputation

Reducing energy waste is one of the primary concerns facing Remote Industrial Plants (RIP) and, in particular, the accommodations and operational plants located in remote areas. With the COVID-19 pandemic continuing to attack the health of workforce, managing the balance between energy efficiency and Occupation Health and Safety (OHS) in the workplace becomes another great challenge for the RIP. Maintaining this balance is difficult mainly because a full awareness of the OHS will generally consume more energy while reducing the energy cost may lead to a less effective OHS, and the existing literature has not seen a system that is designed for the RIPs to conserve energy usage and improve workforce OHS simultaneously. To bridge this gap, in this paper, we propose an AI Empowered Cyber Physical Ecosystem (AECPE) solution for the RIPs, which integrates Cyber-Physical Systems (CPS), artificial intelligence, and mobile networks. The preliminary results of lab experiments and field tests proved that the AECPE was able to help industries reduce the corporate annual energy cost that is worth millions of dollars, optimise the environmental conditions, and improve OHS for all workers and stakeholders. The implementation of the AECPE can result in efficient energy usage, reduced wastage and emissions, environment-friendly operations, and improved social reputation of the industries.

scholarly journals Refrigeration of COVID-19 Vaccines: Ideal Storage Characteristics, Energy Efficiency and Environmental Impacts of Various Vaccine Options

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1849
Author(s):  
Alexandre F. Santos ◽  
Pedro D. Gaspar ◽  
Heraldo J. L. de Souza
Keyword(s):  
Energy Efficiency ◽  
Environmental Impacts ◽  
Thermal Load ◽  
Storage Condition ◽  
Storage Conditions ◽  
Energy Simulation ◽  
Energy Usage ◽  
Impact Simulation ◽  
Storage Characteristics ◽  
The Ideal

This article considers the ideal storage conditions for multiple vaccine brands, such as Pfizer, Moderna, CoronaVac, Oxford–AstraZeneca, Janssen COVID-19 and Sputnik V. Refrigerant fluid options for each storage condition, thermal load to cool each type of vaccine and environmental impacts of refrigerants are compared. An energy simulation using the EUED (energy usage effectiveness design) index was developed. The Oxford–AstraZeneca, Janssen COVID-19 and CoronaVac vaccines show 9.34-times higher energy efficiency than Pfizer. In addition, a TEWI (total equivalent warming impact) simulation was developed that prioritizes direct environmental impacts and indirect in refrigeration. From this analysis, it is concluded that the cold storage of Oxford–AstraZeneca, Janssen COVID-19 and CoronaVac vaccines in Brazil generates 35-times less environmental impact than the Pfizer vaccine.

scholarly journals Evaluation of Sustainable Energy Development Progress in EU Member States in the Context of Building Renovation

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4209
Author(s):  
Rita Remeikienė ◽  
Ligita Gasparėnienė ◽  
Aleksandra Fedajev ◽  
Marek Szarucki ◽  
Marija Đekić ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Renewable Energy Sources ◽  
Investment Efficiency ◽  
The European Union ◽  
Energy Usage ◽  
Member States ◽  
Eu Member States ◽  
The Eu

The main goal of setting energy efficiency priorities is to find ways to reduce energy consumption without harming consumers and the environment. The renovation of buildings can be considered one of the main aspects of energy efficiency in the European Union (EU). In the EU, only 5% of the renovation projects have been able to yield energy-saving at the deep renovation level. No other study has thus far ranked the EU member states according to achieved results in terms of increased usage in renewable sources, a decrease in energy usage and import, and reduction in harmful gas emissions due to energy usage. The main purpose of this article is to perform a comparative analysis of EU economies according to selected indicators related to the usage of renewable resources, energy efficiency, and emissions of harmful gasses as a result of energy usage. The methodological contribution of our study is related to developing a complex and robust research method for investment efficiency assessment allowing the study of three groups of indicators related to the usage of renewable energy sources, energy efficiency, and ecological aspects of energy. It was based on the PROMETHEE II method and allows testing it in other time periods, as well as modifying it for research purposes. The EU member states were categorized by such criteria as energy from renewables and biofuels, final energy consumption from renewables and biofuels, gross electricity generation from renewables and biofuels and import dependency, and usage of renewables and biofuels for heating and cooling. The results of energy per unit of Gross Domestic Product (GDP), Greenhouse gasses (GHG) emissions per million inhabitants (ECO2), energy per capita, the share of CO2 emissions from public electricity, and heat production from total CO2 emissions revealed that Latvia, Sweden, Portugal, Croatia, Austria, Lithuania, Romania, Denmark, and Finland are the nine most advanced countries in the area under consideration. In the group of the most advanced countries, energy consumption from renewables and biofuels is higher than the EU average.

In-situ quantification of NO synthesis in a warm air glow discharge by WMS-based Mid-IR QCL absorption spectroscopy

2022 ◽  
Author(s):  
Chuanqi Wang ◽  
Junjie Qiao ◽  
Yijia Song ◽  
Qi Yang ◽  
Dazhi Wang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Glow Discharge ◽  
Absorption Spectroscopy ◽  
Energy Cost ◽  
High Energy ◽  
Cathode Region ◽  
Synthesis Rate ◽  
No Production ◽  
Airflow Rate

Abstract Nitric oxide (NO) is one of the most crucial products in the plasma-based nitrogen fixation process. In this work, in-situ measurements were performed for quantifying the NO synthesis spatially in a warm air glow discharge, through the method of Mid-infrared quantum cascade laser absorption spectroscopy (QCL-AS). Two ro-vibrational transitions at 1900.076 cm-1 and 1900.517 cm-1 of the ground-state NO(X) were probed sensitively by the help of the wavelength modulation spectroscopy (WMS) approach to increase the signal/noise (S/N) level. The results show a decline trend of NO synthesis rate along the discharge channel from the cathode to the anode. However, from the point of energy efficiency, the cathode region is of significantly low energy efficiency of NO production. Severe disproportionality was found for the high energy consumption but low NO production in the region of cathode area, compared to that in the positive column zone. Further analysis demonstrates the high energy cost of NO production in the cathode region, is ascribed to the extremely high reduced electric field E/N therein not selectively preferable for the processes of vibrational excitation or dissociation of N2 and O2 molecules. This drags down the overall energy efficiency of NO synthesis by this typical warm air glow discharge, particularly for the ones with short electrode gaps. Limitations of further improving the energy cost of NO synthesis by variations of the discharge operation conditions, such as discharge current or airflow rate, imply other effective manners able to tune the energy delivery selectively to the NO formation process, are sorely needed.

Making algorithms safe for workers: occupational risks associated with work managed by artificial intelligence

2021 ◽  
pp. 102425892110350
Author(s):  
Adrián Todolí-Signes
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Health And Safety ◽  
Health Hazards ◽  
Risk Prevention ◽  
Occupational Risks ◽  
Technological Management

It is increasingly common for companies to use artificial intelligence mechanisms to manage work. This study examines the health hazards caused by these new forms of technological management. Occupational risks can be reduced if they are taken into account when programming an algorithm. This study confirms the need for algorithms to be correctly programmed, taking account of these occupational risks. In the same way as supervisors have to be trained in risk prevention to be able to perform their work, the algorithm must be programmed to weigh up the occupational risks – and when such features do not exist, steps must be taken to prevent the algorithm being used to direct workers. The algorithm must assess all (known) factors posing a risk to workers’ health and safety. It therefore seems necessary to incorporate a mandatory risk assessment performed by specialists in the programming of algorithms so that all ascertained risks can be taken into account.

Energy Consumption in Leisure Centres – A Comparative Study

1997 ◽  
Vol 8 (3) ◽  
pp. 207-225
Author(s):  
A.J. Griffiths ◽  
P.J. Bowen ◽  
B.J. Brinkworth ◽  
I.R. Morgan ◽  
A Howarth
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Comparative Study ◽  
Energy Cost ◽  
Good Practice ◽  
The Other ◽  
Occupancy Rate ◽  
The Government ◽  
The Uk ◽  
High Band

The Sports and Recreation sector within the UK uses the equivalent of 3 millions tonnes of coal per year to supply the activities demanded by an ever increasing sports conscience society. The government has attempted to stimulate energy efficiency in this sector through the use of good practice guides and case studies. A comparative study was undertaken to analyse the performance of two leisure complexes in the Seven Valley degree day region. One site had double the occupancy rate of the other. It was found that the energy consumption per user was approximately 10 kWh for both sites. However the energy cost per user showed a large difference: for Site A this index was 31p/user compared to 15p/user at Site B. The primary causes of this difference are attributed to variation in energy mix between the two sites, as well as a difference in the price paid for primary fuel. Indices based on floor area of the facilities exhibit similar trends, and furthermore show that both sites were in the high band of energy consumption. This indicated that both sites had the potential to make significant energy-related savings, and a further breakdown of electrical, natural gas and water consumption per site is used to identify these potential savings in a rapidly expanding sector.

scholarly journals Brain-inspired model for decision-making in the selection of beneficial information patterns among signals received by an unpredictable information-development environment

2021 ◽  
Author(s):  
Alireza Asgari ◽  
yvan beauregard
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health And Safety ◽  
Vital Role ◽  
Successful Implementation ◽  
Development Environment ◽  
Brain Functions ◽  
Operational Costs ◽  
Industrial Operations ◽  
The Brain

With its diversification in products and services, today’s marketplace makes competition wildly dynamic and unpredictable for industries. In such an environment, daily operational decision-making has a vital role in producing value for products and services while avoiding the risk of loss and hazard to human health and safety. However, it makes a large portion of operational costs for industries. The main reason is that decision-making belongs to the operational tasks dominated by humans. The less involvement of humans, as a less controllable entity, in industrial operation could also favorable for improving workplace health and safety. To this end, artificial intelligence is proposed as an alternative to doing human decision-making tasks. Still, some of the functional characteristics of the brain that allow humans to make decisions in unpredictable environments like the current industry, especially knowledge generalization, are challenging for artificial intelligence. To find an applicable solution, we study the principles that underlie the human brain functions in decision-making. The relative base functions are realized to develop a model in a simulated unpredictable environment for a decision-making system that could decide which information is beneficial to choose. The method executed to build our model's neuronal interactions is unique that aims to mimic some simple functions of the brain in decision-making. It has the potential to develop for systems acting in the higher abstraction levels and complexities in real-world environments. This system and our study will help to integrate more artificial intelligence in industrial operations and settings. The more successful implementation of artificial intelligence will be the steeper decreasing operational costs and risks.

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