Cutting the fat from meat processing transport emissions

The Australian Meat Processor Corporation (APMC) and Swinburne University of Technology research project Clean fuels, lower emissions in red meat processing transport looked to develop a system to measure and evaluate transport emissions data for heavy-duty long-distance transport tasks has been completed.

This project saw the development of the Data-Integrated Visualisation and Analytics (DiVA) system to measure transport emissions. This tool, made up of software and hardware solutions, was able to facilitate the collection and storage of GPS, environmental and emissions data with offer cloud storage functionality.

Objectives

The main objective was to develop tools which could provide accurate baseline environmental footprint for red meat processor owned heavy transport tasks. Thus, providing users an important and measurable starting point as they look to reduce greenhouse gas (GHG) emissions.

The solution required integrated functionality to enable efficient and reliable collection, collation, and storage of vehicle and emissions data with data visualisation functionality.

In tandem with the development of this platform, researchers wanted to identify and evaluate immediate, medium- and long-term options available now and for the future, for AMPC members.

Methodology

Consultation and Expressions of Interest (EOI)

The first stage of the project was to engage in consultation with AMPC and their members to identify key requirements. Workshops were conducted to evaluate functional and data requirements and help gain understanding on current sustainability practices. The workshops also served to identify potential trial risks and highlight potential opportunities.

EOI activities were initiated, with two companies selected for the project, with further consultation, candidate vehicle types were then identified. In parallel to this work, an evaluation of commercially available IoT sensors was undertaken.

Solution development

The DiVA solution developed featured:

1. An Internet of Things (IoT) box with various modalities
2. Data-driven capabilities to inform intelligent modelling of GHG emissions
3. DiVA cloud dashboard for data visualisation
4. A portable emission measurement system (PEMS)

As the system required an integrated solution, an IoT box was designed so the system could provide verified emissions data and scope capturing essential baseline data, vehicle performance, GPS location, environmental conditions and scope1 GHG emissions and store the information in the cloud.

Data cloud architecture was designed to perform data acquisition, with data handling broken into different phases to ensure the information could be transmitted safely to the cloud without degradation and loss.

A mini–PEMS was developed to measure emissions gases and enable delivery of real time emission measurement, fuel consumption and performance data for the project.

Verification of data recording and collection was initiated over a series of short and long trips. Once the baseline calculation methods were established, programmers developed automated processes to deliver relevant information which also allowed individual records to be selected and reviewed. Data visualisation of these analytical components was displayed via a dashboard.

Testing

Heavy duty vehicle validation was performed with site visits, first to a vehicle manufacturer supporting this project, and second to two AMPC member companies.

Two tasks were undertaken at the AMPC member companies:

1. Initial field trial deployed the DiVA system in a heavy transport vehicle to capture GPS and vehicle related data only, with data analysis undertaken via the dashboard.
2. Field trials then deployed the DiVA system with all IoT and emissions sensors in heavy-duty transport vehicles. Again, data analysis was undertaken this time with GHG baseline emissions data also present.

The systems were fitted to various vehicles and data collected across several months, with one month’s worth of data analysed.

Outcomes

This project successfully developed a system that could integrate with heavy duty vehicles from various manufacturers to collect important operational performance and emissions data. IoT was effectively integrated into the system for successful upload, retrieval, decoding and display of data to (and from) the cloud.

The results supplied real time insight into the into the performance of the heavy-duty vehicles used in the project.

Utilising the DiVA system with the mini PEMS provided researchers significant operational details and produced an accurate scope 1 GHG and pollutant emissions baseline. This capability providing critical element of measurement for user organisations as a starting point as they look to reduce carbon emissions.

As anticipated, unloaded vehicles produced less emissions. When vehicles were loaded to high capacities, some data from diagnostic systems appears to have a higher degree of uncertainty of measurement. This was seen in the calculated and provided fuel rates on some vehicles. Future research against fuel bowser records could provide more information for researchers on where and why this variance exists.

The work done in this project highlighted many benefits for the AMPC industry, with positive outcomes related to cost saving, maintenance, and sustainability. Amongst the potential benefits:

• Estimation of transportation scope 1 GHG emissions for individual deliveries
• Profiling deliveries for identification of most efficient performance loading configurations
• Greater understanding of emission profiles for various routes
• Ability to deliver indications of vehicle performance for maintenance planning
• Provide information for driver feedback, performance, and training

Recommendations

The DiVA platform proved its capabilities as an integrated method to measure emissions. A first of its type implementation, further potential for the system can be made if greater integration of user logistics, sales and operations data and other parameters are enabled.

It is important to recognise there is no one size fits all solution when it comes to the transition to net zero carbon goals, and each vehicle segment will have its own variables and logistics for consideration. There are considerable different opportunities here, as machine learning and technology options are developed and employed for both short and long-distance application.

Expected project impacts

Land transport of all types is responsible for 15% of all global emissions, which is slightly more than the combined global emissions for the meat and dairy industries.

Emissions from the transport sector are also increasing at the fastest rate of all end-use sectors, whereas emissions from the meat and dairy industries in Australia are reducing. So, it’s critical for the transport sector to rapidly reduce emissions to help keep global temperature rise to 1.5°C.

Advancing the monitoring and management of fuel efficiency and emissions for heavy transport in the red meat industry is a logical step with potentially global benefits. As more global companies across the Agricultural and Transport sectors set science-based targets, our projects’ Industry 4.0 smart technology outcomes can help these companies to develop, monitor, and manage the highest quality standards specifically for reducing red meat industry related heavy transport emissions.

A spokesperson from the Australian Meat Processor Corporation

Download the final report

Click the button below to download Data-integrated Visualisation and Analytics (DiVA) Platform for Transport Emissions, Efficiency and Sustainability (a snapshot version of full report).