Unlock the Potential of Predictive Analytics for Industrial Emissions Monitoring

Direct emissions from industries are a significant contributor to greenhouse gas emissions in the US, ranking as the third largest source, according to the United States Environmental Protection Agency (US EPA). Achieving sustainability and efficiency in industrial manufacturing requires a holistic transformation that encompasses product design, production and factory operations and collaboration with suppliers. Advanced analytics, AI and ML provide the insights needed to make informed decisions and help manufacturers confidently move towards a sustainable future.

Applying AI and ML to solve complex sustainability challenges

Advanced emission monitoring systems harness the power of artificial intelligence (AI), machine learning (ML) and predictive analytics to revolutionize industrial emissions monitoring. Unlike traditional reactive approaches that rely on periodic measurements from costly high-maintenance hardware, these advanced systems continuously collect and analyze parameters in real time.

The backbone for advanced emissions monitoring, reporting and reduction is continuous data collection across production, operations, material, fuel, equipment performance, sensor readings and environmental parameters. This data becomes a source to identify trends, patterns and areas for improvement to reduce emissions.

When paired with real-time, quality data, AI algorithms and advanced mathematical models quickly identify deviations, proactively alert against thresholds and remain nimble to address new challenges and evolving regulations. AI and ML models are revolutionizing our ability to monitor and estimate emissions levels of gases such as Nitric Oxide (NOx), Sulfur Dioxide (SO2), Carbon Monoxide (CO), Carbon Dioxide (CO2), hydrocarbons (HC) and more.

Benefits of implementing an AI and ML-based emissions monitoring system

Advanced AI and ML-based emissions monitoring systems can help industrial manufacturers make informed choices on reducing their emissions with predictive insights and support their sustainability reporting and transformation goals. At the same time, they drive operational efficiency and cost savings.

1. Accuracy in emissions monitoring. By using real-time data and predictive analytics, these advanced systems provide unparalleled accuracy in monitoring emissions. By identifying emission patterns and trends, organizations can fine-tune their emission reduction strategies. This precision allows them to meet stringent regulatory standards and contribute to a cleaner environment.
2. Cost savings. By helping to optimize energy usage and reducing waste, it helps to lower operating costs of industrial production. Also, proactive maintenance and early detection of potential issues prevent costly downtime and unplanned maintenance interventions.
3. Regulatory compliance. With continuous and accurate data collection, organizations can effortlessly generate reports and meet environmental regulatory requirements. This proactive approach minimizes the risk of penalties, fines or reputational damage due to non-compliance.
4. Improved process safety. Advanced monitoring systems can detect potential hazards and anomalies in real time, allowing for prompt intervention and mitigation. This helps to make the working environment safer by reducing the risk of industrial accidents.
5.  User-friendly interface and remote accessibility. Most advanced emissions monitoring systems in the market come with a user-friendly interface, easy navigation and data interpretation that allows even non-technical personnel to operate the system effectively. Moreover, it’s easier to support remote monitoring and control emissions across production sites in real time from any location. Getting started with an AI and ML-based emissions monitoring system

It's important to consider a holistic approach when designing your AI and ML-based emissions monitoring system. This includes data modeling and analysis, technology and change management to integrate this into your overall sustainability transformation plans. Here’s a comprehensive approach to getting started with your advanced emissions monitoring system:

1. Data collection. This is a crucial first step in model development. It involves the aggregation of diverse historical data to understand past trends and conditions. It encompasses historical emission data, operational variables like temperature, pressure, and flow rates, meteorological conditions, and fuel usage details. Additionally, it includes production metrics, equipment performance data, real-time sensor readings, maintenance records, operational data and environmental quality indicators. This extensive data gathering provides a deep insight into the factors that influence system performance, setting the foundation for effective model building.
2. Data pre-processing. Clean and filter the data to remove anomalies like missing values and outliers, normalize data, remove duplicates and enhance data quality through techniques like labeling and augmentation.
3. Feature engineering. Extract relevant features from the data, identifying trends, patterns and correlations. You may also create new features through mathematical transformations or domain expertise.
4. Model selection. Choose the right machine learning algorithms, such as linear regression, decision trees or neural networks, based on your project requirements.
5. Model training. Split the data into training, validation and testing sets (typically 70% 15%, and 15% respectively) and train the model on these datasets.
6. Model evaluation. Assess the model’s performance using metrics like mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared.
7. Model deployment and maintenance. Once the model is trained and evaluated, deploy it into production. Continuous monitoring of model performance and data quality is essential, along with periodic updates and retraining to adapt to new data or conditions.

We are a leader in sustainability services

NTT DATA has proven experience of helping our clients reduce their industrial combustion emissions while driving cost efficiency with the use of advanced emissions monitoring systems as part of a holistic approach to a data-driven sustainability transformation journey. Our more than 15 years of sustainability services experience, 1500+ successful sustainability projects, over 400 dedicated sustainability experts and industry-leading investments in sustainability and R&D provide an unparalleled advantage to our clients. We have been recognized as a leader and star performer in Everest Group’s Sustainability Enablement Technology Services PEAK Matrix® Assessment 2024. Want to learn more about how we can help you achieve your corporate sustainability goals at speed and scale? Contact us to schedule a session with our consulting team or visit our Sustainability Services webpage.