Co-pyrolysis of biomass feedstocks with polymeric wastes is a promising strategy for improving the quantity and quality parameters of the resulting liquid fuel. Numerous experimental measurements are typically conducted to find the optimal operating conditions. However, performing co-pyrolysis experiments is highly challenging due to the need for costly and lengthy procedures. Machine learning (ML) provides capabilities to cope with such issues by leveraging on existing data. This work aims to introduce an evolutionary ML approach to quantify the (by)products of the biomass-polymer co-pyrolysis process. A comprehensive dataset covering various biomass-polymer mixtures under a broad range of process conditions is compiled from the qualified literature. The database was subjected to statistical analysis and mechanistic discussion. The input features are constructed using an innovative approach to reflect the physics of the process. The constructed features are subjected to principal component analysis to reduce their dimensionality. The obtained scores are introduced into six ML models. Gaussian process regression model tuned by particle swarm optimization algorithm presents better prediction performance (R2 > 0.9, MAE < 0.03, and RMSE < 0.06) than other developed models. The multi-objective particle swarm optimization algorithm successfully finds optimal independent parameters.

Rice University chemists are employing machine learning to fine-tune its flash Joule heating process to make graphene. A flash signifies the creation of graphene from waste. Rice University scientists are using machine learning techniques to streamline the process of synthesizing graphene from waste through flash Joule heating. This flash Joule process has expanded beyond making graphene from various carbon sources, to extracting other materials, like metals, from urban waste. The technique is the same for all of the above: blasting a jolt of high energy through the source material to eliminate all but the desired product.

For my final capstone project in Flatiron School's Immersive Data Science Program, I decided to test my newfound skills and continue furthering my personal investigations into the relationships that exist between data, waste, and energy. Recently, I have been learning more about the various ways that Municipal Solid Waste (MSW) can be transformed into energy. The most promising and efficient technology that I have come across to date is Plasma Arc Gasification. In my research, I discovered that understanding specific composition details about the MSW to be used as feedstock is one of many critical steps in designing a plasma gasification facility. What I set out to do for my capstone project, was to see if I could find some MSW collection datasets and perform a Feedstock Analysis with the intent of calculating specific Waste Type Compositions, Energy Density (kWh/kg), and Total Energy (kWh) for each sample.

An artificial intelligence-driven compliance technology company founded by a former fund manager and corporate broker has raised £2.5 million in a recent funding round. FeedStock's latest funding round was led by Praetura Ventures, with Force Over Mass and existing investor Illuminate Financial Management also participating in the round. The company provides AI and natural language processing technologies to help both the buy- and sell-side institutions meet various compliance requirements, as well as commercial goals. Founded in 2015, FeedStock was established by former analyst and fund manager at GAM, Lucas Wurfbain, alongside Charlie Henderson, who previously worked as a research analyst and corporate broker. "With our background in highly regulated businesses, we are seeing enormous appetite for our proprietary technology; not only from businesses required to comply with MiFID II, but also for enterprises that are looking to leverage AI as a core component of their business for efficiency gains and revenue generation," Henderson commented on the recent investment.