With each passing day, the reality of climate change becomes deeper. Harsh weather events occur in places that have always been calm. Unexpected droughts lead to food crises and health emergencies.
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No sector can afford not to take active steps to mitigate the situation, least of all technology. It wouldn’t be an overstatement to say that the world is looking at tech experts to create a powerful solution that heals the planet.
While climate change does not have a silver bullet, one accessible area to consider is emission control.
A Nature Reviews Earth & Environment study highlights that global CO2 emissions in 2024 totaled 36.3 Gt CO2. It is likely that we have already exhausted the carbon budget to stay below the 1.5°C threshold. Untreated or uncontrolled emissions have also started to manifest as serious health issues among exposed communities.
How can more advanced emission control algorithms help us handle these circumstances? Read on.
1. AI-Driven Regression Algorithms
Industrial establishments have to monitor their emission levels to ensure compliance with regulatory requirements. Traditionally, they use continuous monitoring systems that gather and analyze data, interacting with sensors to check the concentration and flow rate.
These systems work well until they don’t. For example, the recent Sterigenics Atlanta case has exposed the dangers of non-compliant industries. Some community members report facing increased cancer risk due to excessive exposure to ethylene oxide.
According to TorHoerman Law, the outcry stems from the medical sterilization facility’s alleged failure to meet regulatory requirements.
Enter regression algorithms. These approaches use supervised learning to estimate emissions of CO, CO2, and NOx, based on input factors. The variables could be diverse, from ambient temperature and steam production to the type of fuel used.
A 2024 Heliyon study found that machine learning (ML) algorithms for regression analysis enhance the efficiency of equipment and reduce its pollution load.
As a tech expert, your team can implement regression algorithms using popular programming languages like Python and R. The latter has excellent visual capabilities and supports most statistical requirements.
2. Explore the Shuffled Frog Leaping Algorithm
Sometimes, the optimal solution lies in exploring an all-new approach. What if an altogether new algorithm could be the answer to controlling (and even lowering) emissions?
In 2023, the Journal of Electrical and Computer Engineering published an insightful study on employing a shuffled frog-leaping algorithm for minimizing total CO2 emissions. This approach determines the best start-up and shut-down times for the emission-generating units.
The outputs can help an establishment to reduce CO2 emissions and operating costs. The results agreed with the researchers’ expectations.
This metaheuristic approach is proving path-breaking for many initiatives, from unmanned aerial vehicle routing to solving the economic dispatch problem. Implementing it for emission control will need clear definitions of the groups and the approaches for shuffling or re-grouping them.
3. AI for Anomaly Detection and Predictive Maintenance
Artificial intelligence is also reshaping emission control algorithms, optimizing them for efficiency and fault detection.
AI interventions can be particularly impactful in detecting anomalies stemming from faults or leaks. With pattern analysis, AI tools can recognize deviations from standard behavior and issue alerts. This mechanism can expedite corrective action.
It doesn’t stop there. After adopting Air-powered algorithms for emission control, your organization can also experience an edge in ensuring regulatory compliance and automating reporting.
According to MarketsAndMarkets data, this field is expected to evolve with AI-linked sensors and AI-driven carbon capture technologies.
Perhaps the true magic lies in AI’s capacity to reduce Scope 3 emissions. EY labels these the hidden climate costs of business, noting that they dwell in various components of the value chain. With tools driven by artificial intelligence, businesses can work with multiple suppliers simultaneously and more efficiently, lowering emissions at all stages.
According to Oracle, an AI-powered maintenance cycle can let you evolve from preventative to predictive. This is because the volume of data these algorithms can analyze is tremendous. It is much more comprehensive than basing maintenance on fixed cycles, irrespective of equipment usage or ambient factors.
Upskilling Ideas for Tech Professionals in Emission Control
In light of these developments, professionals should consider upskilling to contribute meaningfully to efforts for reducing harmful emissions.
- SaaS (software-as-a-service) competencies to enable enterprises to reduce their carbon footprint
- Machine learning algorithms
- Environmental regulations and compliance
- Data privacy and security (crucial while handling vast amounts of emissions data)
More organizations are offering opportunities to their workforce to develop these skills and other capacities aligned with global environmental needs. Besides being empowering, they can also make employees less apprehensive of AI advancements that threaten job loss and redundancies.
The world continues to warm as you read this. It is now urgent to strategize and implement far-reaching solutions before the situation gets completely out of hand. A nexus between scientific research and technology shows promise for this goal and is worth pursuing.
