Artificial Intelligence can reliably help industrial plants go green
Due to the public outcry and tax cuts, companies are going green with AI to prevent irreversible harm to the planet.
We are the first generation to witness the effects of climate change unfold, and the last with the ability to prevent those effects from intensifying and irreversibly harming our planet.
According to the US Environmental Protection Agency, global carbon emissions from fossil fuels rose significantly since 1900. CO2 emissions have increased by about 90% since 1970, with emissions from fossil fuel combustion and industrial processes contributing about 78% of the total greenhouse gas emissions increase from 1970 to 2011.
These insights are not new to operators of industrial plants and factories. China is followed by the US and the EU as the largest emitters of CO2. They collectively account for over 50% of global emissions.
Most industrial facilities are well aware of their carbon footprint. Due to public demand for environmental sustainability and higher taxes on emissions, they have every intention to implement decarbonization measures. For the most part, however, their efforts have fallen short, leaving a gap between their ambitions and the reality of their carbon reduction activities.
According to the Boston Consulting Group, the standard three-step approach to implementing decarbonization measures starts with assessing the carbon footprint and comparing it against the carbon footprint of industry peers, as well as national/international standards. The next step is to develop realistic short- and long-term decarbonization targets based on a thorough cost-benefit analysis. The final step is to define the specific measures that will provide environmental and economic benefits.
Most industrial plants and factories already have the necessary capabilities to go green, both in terms of production and logistics. Depending on the industry, the necessary change may be a switch in energy sources, such as solar and wind in place of coal and fossil fuels. In most cases, however, the change calls for an upgrade in processes and technologies for increased efficiency. Some companies, such as the Israel-based EcoPlant, are working to tackle that challenge before it’s too late.
EcoPlant, which has offices in Israel and the US, developed a SaaS (Software as a Service) solution that connects to air compressors’ controllers and property sensors to monitor and continuously optimize entire systems. Their predictive AI algorithms conduct ongoing energy surveys and dynamically control each compressor and the whole system.
The end result? Significantly reduced energy waste, increased reliability, and optimized maintenance. Such a solution enables continuous savings of hundreds of thousands of dollars annually. EcoPlant’s proprietary algorithms can also be applied in settings such as hospitals and commercial buildings, as well as critical infrastructure equipment, such as pumps and chillers, essentially making it a one-size-fits-all solution.
The harmful effects that stem from energy waste and CO2 emissions undoubtedly trace back to human decisions and errors. Undoing those negative effects, especially in the modern post-pandemic world, requires the collaborative efforts of human intelligence with machine intelligence. Numerous facilities hailing from industries with the largest carbon footprints, such as technology, automotive and process industries, are already getting on board with AI solutions such as EcoPlant, to meet popular and regulatory demands.
According to the Paris Climate Agreement, which is an ambitious global action plan to fight climate change and its negative impact, emissions from production and logistics would need to drop approximately 45% by 2030 to be on a path to meet the 1.5°C target for limiting the global temperature increase. To meet this goal, it is imperative for industrial plants and factories to ramp up their efforts to go green by turning to AI.