IDARE Enterprise AI predictive analytics platform background

Use Case

Predicting CO2 Emissions in an Industrial Process

Accurately forecasting CO2 emissions in industrial processes is crucial for regulatory compliance, sustainability, and process optimization.

1Overview & Strategic Importance

Predicting CO2 Emissions in an Industrial Process

Regression Solution Emission Control

Problem Statement

Predicting CO₂ emissions is crucial for environmental sustainability, regulatory compliance, and industrial efficiency. Industries must monitor emissions to reduce impact and meet carbon footprint regulations. Emission levels are influenced by fuel composition, temperature, pressure, and process efficiency. Addressing this supports greener industrial transition.

Required Solutions

Developing ML models for CO₂ forecasting.
Identifying contributors like temperature and pressure.
Optimizing process efficiency for compliance.

Solution Objectives

Identify patterns and key contributors via EDA.
Develop models to estimate emissions based on variables.
Conduct scenario analysis for impactful factors.
Assist industries in monitoring and reducing footprint.

Understanding the Problem

Emissions are affected by fuel composition, chemical reaction efficiency, and system pressure.

AI models analyze historical trends to implement proactive measures to minimize industrial carbon footprints and ensure regulatory compliance.

2About the Data

Data Collection

The dataset includes operational parameters from plant units, measuring chemical concentrations, temperatures, and flow rates.

Major Parameters Description

Download Training Data

Carbon dioxide CO2

Measured CO2 emission level in the plant.

Ammonia NH3

Ammonia concentration in the process.

C4H11NO

2-Amino-2-methylpropanol used in capture.

Piperazine C4H10N2

Absorption promoter in CO2 capture.

PI-2

Process indicator for stage 2.

TI-2

Temperature at stage 2.

FI-2

Flow rate at stage 2.

PI-3

Process indicator for stage 3.

TI-3

Temperature at stage 3.

3Using iDareAI

Guided Mode Initialization

AUploading Dataset

Click on the **'Upload CSV or Excel Data'** button → Select a source for the dataset → Upload `co2_emission_train.csv`. The system automatically analyzes the file, extracts column descriptions, and identifies the top value-adding targets for prediction.

BChoosing Analysis Mode

Choose between autonomous machine learning or manual building. In autonomous mode, ask questions such as:

What is the maximum amount of CO2 emitted according to this dataset?
Which factors impact the emitted amount of CO2?

Operation Using Autonomous Guided Mode

AQuery Response

Maximum CO2 emitted is predicted to be 0.13 based on LightGBM. Random Forest model provided a more reliable prediction with relatively lower error rates.