Artificial intelligence Mogul

AI-driven solutions for a greener, more sustainable future.

Who is AI Mogul?

AI Mogul has over 30 years of experience compiling Big Data into cohesive AI algorithms used across 33 industries.

With several ongoing AI development and environmental impact projects in place, we are dedicated to identifying opportunities that allow the development of powerful Big Data analysis algorithms, fostering innovation to reduce environmental impact in manufacturing and gathering data that can promote environmentally friendly consumer practices.

Projects by AI Mogul

Predictive Analysis of Large Data for Anomaly Detection
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Big Data for Lithium-ion Battery Certificate in Sustainable Circular Economy

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Managing Industrial Waste Materials Using a Decision Workflow Methodology.

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Addressing the Impact of Single-use Plastic Water Bottles (SUPBW)
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Predictive Analysis of Large Data for Anomaly Detection.

In 2020, Sarah Sajedi (CEO of AI Mogul) and ERA Environmental, alongside NSERC and Concordia University, began working on a tool that uses AI technology to detect anomalies in data in environmental reports prepared by automotive companies. In the project’s early years, this proof of concept involved studying an automotive company’s data (Fiat Chrysler Automobiles (FCA)) and using rudimentary statistical models to attempt pattern detection and identify outliers. Once the model was established, the project evolved to allow automotive companies to monitor their environmental reporting data, such as tracking the amount of paint used in each plant and the emissions released.

In 2022, Sarah Sajedi (CEO of AI Mogul), ERA and the Concordia team finetuned the model until it could effectively flag anomalous data. Together, they published an academic journal article highlighting the challenge sin adopting artificial intelligence-based user input verification frameworks in reporting software systems and set out to continue the study by applying the developed algorithm to various types of automotive company data, including data coming from Toyota Motor Manufacturing Kentucky (TMMK) and FCA. Presently, the model can accurately detect anomalies across four different features: product usage, the quantity of the vehicles produced, the correlation between usage and production, and the percentage difference between usage and production. And by using the AI system's algorithm scoring, the model can also flag discrepancies in production and usage data. The study will continue until the model is fully integrated into reporting software systems and used by manufacturing companies to detect anomalous environmental data.

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Big Data for Lithium-ion Battery Certificate in Sustainable Circular Economy: from Mine to Pack in Collaboration with Concordia University.

This partnership between AI Mogul and the Department of Chemical and Materials Engineering (CME) at Concordia University intends to trace energy consumption and CO2 emissions throughout the production of a Lithium-ion (Li-ion) battery, from mining to pack assembly to recycling, using AI technology at each step of the process to compile the data into a predictive analysis software and make the creation of a battery certificate possible. By optimizing the Li-ion battery production process and promoting a zero-waste manufacturing framework, this project will reduce the time taken to develop materials as well as the cost of cell manufacturing. 

The study seeks to categorize all aspects of battery production and origin to predict a single battery’s potential environmental footprint, thus enabling AI technology to examine the variables and extrapolate the projected impact to more large-scale battery production operations. As the world moves towards increased electric vehicle production, building more and more battery packs (and more and more cells into each pack), there will be an exponential uptick in the volume of data required. Manually measuring the impact produced by billions of batteries is not feasible, which is why the project aims to establish a benchmark by analyzing the impact from a smaller (though sizeable) number of batteries produced in a laboratory setting. AI Mogul will be instrumental in co-designing the project to include the critical AI tracking component at each step. The gathered empirical data will be analyzed and utilized to benchmark the impact of different types of batteries at each stage, along with fully designing and developing software that companies can use to conduct Big Data analysis for their battery production processes to create a battery certificate.

Conferences

ABAA13 - October 2022 | Marrakesh, Morocco

(Advanced Li-ion Batteries for Automotive Applications)
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Impulsion - March 2023 | Montreal, Quebec

(International Summit on Electric and Smart Transportation)


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CQLMNS - June 2023 | Paris, France

International Workshop on the Characterization and Quantification of Lithium, from the Micro- to the Nano-Scale, from Mining to Energy.

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Managing Industrial Waste Materials Using a Decision Workflow Methodology to Help Manufacturers Fulfill UN Sustainable Development Goals.

Packaging waste has become more prevalent over the past ten years as a result of the increase in manufactured products. Proper solid waste management is a crucial component of waste minimization and impacts how the material is frequently directed to waste handling methods with a negative environmental impact. AI Mogul is researching ways to provide companies and individuals with guidance on "what todo" with these waste materials to prevent them from ending up in landfills or being incinerated to produce energy (a source of air pollution), all the while helping them achieve UN Sustainable Development Goals (SDG) 11 and 12.

Currently, the team is preparing a detailed publication that evaluates all waste minimization aspects, including identifying different waste materials produced by various companies, formulating a workflow and decision tree that relate the type of waste to a feasible waste minimization recommendation to the same ability as a seasoned sustainability expert, and facilitating the implementation of these practices in many target companies.

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Addressing the Impact of Single-use Plastic Water Bottles (SUPBW).

The AI Mogul team is spearheading a comparative study of Corporate ESG reports and Consumer behaviour to raise awareness among consumers and corporations about social responsibility and the health and environmental impacts of SUPBWs. This study explores consumers' knowledge of the effects of SUPBWs on human and environmental health, how their habits contribute to plastic bottle pollution, and how various companies are handling their water bottle production by evaluating their efforts to reduce plastic bottle waste (as evidenced by their corporate ESG reports).

With the gathered knowledge, the team aims to increase consumer awareness of available alternative options and directly address the main driver for the use of SUPBWs.  The team will also prioritize innovations aiming to provide alternative options to not only reduce and recycle but ultimately completely replace SUPWBs.

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