Melbourne-based renewable energy company, AgBioEn, is using a combination of IoT devices, drones, and telemetry as part of an AUD 2 billion initiative to turn agricultural waste into renewable fuels.
The company plans to take waste from high-yielding, sustainably-grown crops and then process it to produce renewable diesel, bio-jet fuel, LPG, heat (for on-farm glasshouses), food-grade liquified CO2, and a soil nutrient that can be plowed back to grow more crops.
AgBioEn has three key divisions: Agriculture, Bioenergy, and Environment. The Agriculture division is currently exploring innovative ways to grow better crops that achieve an increased yield in the grain or cobb, higher biomass, more carbon sequestered in the soil, and use less water.
Working with La Trobe University and Microsoft partner LAB3, AgBioEn is running a series of on-farm trials, growing different crops under different conditions.
It has used IoT sensors and devices to monitor the health and growth of the crops with telemetry and drone data held in an Azure Cloud Edge Computing Platform. Data analytics tools and dashboards are available for La Trobe researchers and farmers that partner with the company.
Crop trials will continue for the next three to four years, while AgBioEn will complete its fuel manufacturing facility design this year, with construction scheduled to start early next year with fuel produced from 2023.
Over the next four to five years, AgBioEn plans to invest AUD 1.2 billion in its Renewable Energy and Fuels Facility to turn agricultural waste (biomass) into fuel and a further AUD 800 million in 100,000 hectares of cropping land. Its plan is for agricultural waste (biomass) sourced from farms within a 200 km radius of the fuels facility to be turned into renewable fuels.
Ultimately it plans to produce 150 million liters of fuel each year.
LAB3 has helped design the crop testing system, which sees data from farm-based sensors and drones uploaded via LoRaWan networks into an Azure IoT Hub.
Stream analytics performs an initial analysis of the stored data in an Azure Data Lake and available for analysis through Azure Cosmos DB.
