In Texas, Texas A&M AgriLife Research scientists are using artificial intelligence to set a new world record for producing algae as a reliable, economic source for biofuel that can be used as an alternative fuel source for jet aircraft and other transportation needs. Joshua Yuan, AgriLife Research scientist, professor and chair of Synthetic Biology and Renewable Products in the Texas A&M College of Agriculture and Life Sciences Department of Plant Pathology and Microbiology, is leading the research project. "The commercialization of algal biofuel has been hindered by the relatively low yield and high harvesting cost," Yuan said. "The limited light penetration and poor cultivation dynamics both contributed to the low yield." Overcoming these challenges could enable viable algal biofuels to reduce carbon emissions, mitigate climate change, alleviate petroleum dependency and transform the bioeconomy, Yuan said.

Algae has such immense potential as a biofuel source that scientists have long been studying it for sustainable energy. They even created 3D printed artificial leaves out of algae to produce oxygen for our investigations of Mars. Now, scientists from Texas A&M AgriLife Research are using artificial intelligence to break a new world record for producing algae as a reliable biofuel source, so that a greener and more economical fuel source for jet aircraft and other kinds of transportation could be achieved. The research project is conducted by Joshua Yuan, PhD., and funded by the U.S. Department of Energy Fossil Energy Office. One of the major problems with algaes' prominence was their growth limitations due to mutual shading and the high cost of harvest.

Algae are a varied category of aquatic plant-like creatures. Phytoplankton is a term used to describe oceanic algae. These basic creatures generate energy from sunlight through photosynthesis, which allows them to manufacture carbohydrates, oils, and proteins. These can then be processed to produce a third-generation biofuel. Biofuel is any fuel derived from living things or living things' waste products (like fecal matter or urine).

The goal of Kaare Hartvig Jensen, Associate Professor at DTU Physics, was to reduce the need for harvesting, transporting, and processing crops for the production of biofuels, pharmaceuticals, and other products. The new method of extracting the necessary substances, which are called plant metabolites, also eliminates the need for chemical and mechanical processes. Plant metabolites consist of a wide range of extremely important chemicals. Many, such as the malaria drug artemisinin, have remarkable therapeutic properties, while others, like natural rubber or biofuel from tree sap, have mechanical properties. Because most plant metabolites are isolated in individual cells, the method of extracting the metabolites is also important, since the procedure affects both product purity and yield.

Plant metabolites consist of a wide range of extremely important chemicals. Many, such as the malaria drug artemisinin, have remarkable therapeutic properties, while others, like natural rubber or biofuel from tree sap, have mechanical properties. Because most plant metabolites are isolated in individual cells, the method of extracting the metabolites is also important, since the procedure affects both product purity and yield. Usually the extraction involves grinding, centrifugation, and chemical treatment using solvents. This results in considerable pollution, which contributes to the high financial and environmental processing costs.

New Delhi, Jul 02: Bio-derived fuels are gaining widespread attention among the scientific community across the world. The work on biofuels is in response to the global call for reducing carbon emissions associated with the use of fossil fuels. In India too, biofuels have caught the imagination of researchers. For instance, researchers of the Indian Institute of Technology (IIT) Hyderabad have started using computational methods to understand the factors and impediments in incorporating biofuels into the fuel sector in India. A unique feature of this work is that the framework considers revenue generation not only as an outcome of sales of the biofuel but also in terms of carbon credits via greenhouse gas emission savings throughout the project lifecycle.

IIT Hyderabad Researchers are using computational methods to understand the factors and impediments in incorporating biofuels into the fuel sector in India. This work has been spurred by the increasing need to replace fossil fuels by bio-derived fuels, which, in turn, is driven by the dwindling fossil fuel reserves all over the world, and pollution issues associated with the use of fossil fuels. The model developed by the IIT Hyderabad team has shown that in the area of bioethanol integration into mainstream fuel use, the production cost is the highest (43 per cent) followed by import (25 per cent), transport (17 per cent), infrastructure (15 per cent) and inventory (0.43 per cent) costs. The model has also shown that feed availability to the tune of at least 40 per cent of the capacity is needed to meet the projected demands. A unique feature of this work is that the framework considers revenue generation not only as an outcome of sales of the biofuel but also in terms of carbon credits via greenhouse gas emission savings throughout the project lifecycle.

Algae, that green scum often seen on the surface of ponds, and credited with harmful ocean algal blooms that kill ocean life might just hold an important key to addressing climate change. Algae, much like trees, uses carbon dioxide to conduct photosynthesis, sequestering CO2 as it grows. Hypergiant, an AI products and solutions company, is harnessing this unique power of algae in its latest technology, the EOS bio-reactor which uses AI to optimize algae growth and carbon sequestration. Its bio-reactor is built to hook up to HVAC systems found in large industrial buildings, skyscrapers and apartment buildings which are some of the biggest contributors to global warming from the CO2 emitted through their energy usage and air conditioning systems. The science is clear that we must not only cut our carbon emissions as a means to stop the irreversible harm of climate change and limit global warming but that we also need to take carbon out of the atmosphere to stay within the stated target 1.5 C of the Paris Climate Agreement.

Digital agriculture has the promise to transform agricultural throughput. It can do this by applying data science and engineering for mapping input factors to crop throughput, while bounding the available resources. In addition, as the data volumes and varieties increase with the increase in sensor deployment in agricultural fields, data engineering techniques will also be instrumental in collection of distributed data as well as distributed processing of the data. These have to be done such that the latency requirements of the end users and applications are satisfied. Understanding how farm technology and big data can improve farm productivity can significantly increase the world's food production by 2050 in the face of constrained arable land and with the water levels receding. While much has been written about digital agriculture's potential, little is known about the economic costs and benefits of these emergent systems. In particular, the on-farm decision making processes, both in terms of adoption and optimal implementation, have not been adequately addressed. For example, if some algorithm needs data from multiple data owners to be pooled together, that raises the question of data ownership. This paper is the first one to bring together the important questions that will guide the end-to-end pipeline for the evolution of a new generation of digital agricultural solutions, driving the next revolution in agriculture and sustainability under one umbrella.

Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems from the fact that a liquid phase upgrading using mineral acid is a cost-effective chemical transformation. In this chemical upgrading process, adding a proton (positively charged atomic hydrogen) to an oxygen atom is a central step. The protonation energies of oxygen atoms in a molecule determine the thermodynamic feasibility of the reaction and likely chemical reaction pathway. A quantum chemical model based on coupled cluster theory is used to compute accurate thermochemical properties such as the protonation energies of oxygen atoms and the feasibility of protonation-based chemical transformations. However, this method is too computationally expensive to explore a large space of chemical transformations. We develop a graph neural network approach for predicting protonation energies of oxygen atoms of hundreds of bioxygenate molecules to predict the feasibility of aqueous acidic reactions. Our approach relies on an iterative local nonlinear embedding that gradually leads to global influence of distant atoms and a output layer that predicts the protonation energy. Our approach is geared to site-specific predictions for individual oxygen atoms of a molecule in comparison with commonly used graph convolutional networks that focus on a singular molecular property prediction. We demonstrate that our approach is effective in learning the location and magnitudes of protonation energies of oxygenated molecules.