Short answer

Integrate mechanistic modelling into the design process for food supply chains to quantitatively assess and optimise for carbon emission reduction and energy efficiency.

Field: Sustainability
Source: Chemical Engineering Journal (2023)
Method: Mechanistic modelling integrating chemical engineering process design, heat exchange principles, and empirical modelling.
Evidence: Strong effect

Mechanistic modelling of dairy manufacturing and distribution can predict energy consumption and carbon emissions, enabling strategic planning for net zero targets. This sustainability research insight is drawn from a 2023 study published in Chemical Engineering Journal. Using Mechanistic modelling integrating chemical engineering process design, heat exchange principles, and empirical modelling., researchers explored how this design variable affects real-world outcomes. The key design takeaway: Integrate mechanistic modelling into the design process for food supply chains to quantitatively assess and optimise for carbon emission reduction and energy efficiency.

Study

SustainabilityRecentStrong effect

Dairy Industry's Net Zero Pathway: Modelling Energy Consumption for Decarbonisation

Mechanistic modelling of dairy manufacturing and distribution can predict energy consumption and carbon emissions, enabling strategic planning for net zero targets.

Chemical Engineering Journal · 2023

Key Findings

01Embodied energy for skimmed milk ranged from 309 to 869 kJ/L across tested scenarios.
02Up to 90.2% carbon emission reductions are achievable by 2050 through strategic energy and distribution choices.
03The model demonstrates flexibility in projecting energy consumption and carbon emissions under diverse scenarios.

Application

Design takeaway

Integrate mechanistic modelling into the design process for food supply chains to quantitatively assess and optimise for carbon emission reduction and energy efficiency.

How to apply

Use simulation tools to model the energy consumption and carbon footprint of proposed manufacturing and distribution systems, testing various combinations of energy sources, technologies, and logistics before implementation.

Project actions

01When modelling, clearly define the boundaries of your system (what's included and excluded).
02Consider the full lifecycle impact, from raw material sourcing to end-of-life, if possible.

Method & Evidence

AimTo develop and validate a mechanistic energy consumption model for dairy manufacturing and distribution to support net zero carbon emission strategies.

MethodMechanistic modelling integrating chemical engineering process design, heat exchange principles, and empirical modelling.

ProcedureA model was developed to simulate energy consumption for skimmed milk and cream manufacturing and distribution. Twelve scenarios were tested, varying fuel types for heating (oil, natural gas, hydrogen), refrigerated vehicle types (diesel, electric), and distribution infrastructures (centralised, decentralised). Projections to 2050 were made using UK electricity carbon conversion factors.

ContextDairy manufacturing and distribution supply chains.

Variables

IV["Fuel type for heating (oil, natural gas, hydrogen)","Refrigerated vehicle type (diesel, electric)","Distribution infrastructure (centralised, decentralised)"]

DV["Energy consumption (kJ/L)","Carbon emissions (e.g., kg CO2e/L)"]

CV["Product type (skimmed milk, cream)","Manufacturing process","Distribution distance (implicitly, through infrastructure type)","Timeframe for 2050 projections (UK electricity carbon conversion factor)"]

Strengths & Limitations

Strengths

+Mechanistic modelling provides a detailed, process-based understanding.
+Scenario analysis allows for comprehensive exploration of design alternatives.
+Future projections add strategic value for long-term planning.

Limitations

The complexity of real-world supply chains can be difficult to fully capture in a model. Data availability and accuracy can also be a challenge.

Reliability & validity

The reliability of the model depends on the accuracy of the underlying physical principles and empirical data used. Validity is supported by the scenario analysis and the alignment with global net zero priorities.

Think critically

How might the 'mechanistic modelling' approach be adapted for a different industry, such as electronics manufacturing or fashion retail, to achieve similar net zero goals?

Design Principles

"Quantify and model energy consumption and carbon emissions across the entire product lifecycle to inform sustainable design decisions."

This research provides a robust framework for understanding and reducing the significant carbon footprint of the dairy sector. By simulating various energy sources and distribution methods, designers and engineers can make informed decisions to transition towards sustainable practices and achieve ambitious decarbonisation goals.

What This Means for Your Design

This study shows how to use computer models to figure out the best ways for dairy companies to make and move their products with the least amount of carbon pollution, helping them reach 'net zero' goals.

How to use in your project

1.Use the concept of mechanistic modelling to justify your approach to analysing energy consumption or environmental impact in your design project.
2.Refer to the findings on emission reduction percentages to support the significance of your own design's potential environmental benefits.

Add to My Project

Quick Cite

Paragraph starter

This research demonstrates the utility of mechanistic modelling in assessing and reducing the environmental impact of industrial processes. By simulating various operational scenarios for dairy manufacturing and distribution, the study quantified potential carbon emission reductions, providing a data-driven roadmap towards net zero targets. This approach highlights the importance of integrating detailed process understanding with predictive modelling to inform sustainable design strategies and achieve significant environmental benefits.

Source

Chemical Engineering Journal

Net zero roadmap modelling for sustainable dairy manufacturing and distribution

journal · 2023

View source

Questions About This Research

What does the research say about dairy industry's net zero pathway: modelling energy consumption for decarbonisation?: Integrate mechanistic modelling into the design process for food supply chains to quantitatively assess and optimise for carbon emission reduction and energy efficiency. Evidence: Chemical Engineering Journal (2023).
Why does "Dairy Industry's Net Zero Pathway: Modelling Energy Consumption for Decarbonisation" matter for design?: This research provides a robust framework for understanding and reducing the significant carbon footprint of the dairy sector. By simulating various energy sources and distribution methods, designers and engineers can make informed decisions to transition towards sustainable practices and achieve ambitious decarbonisation goals.
How can designers apply this research?: Integrate mechanistic modelling into the design process for food supply chains to quantitatively assess and optimise for carbon emission reduction and energy efficiency.
What were the main findings?: Embodied energy for skimmed milk ranged from 309 to 869 kJ/L across tested scenarios.. Up to 90.2% carbon emission reductions are achievable by 2050 through strategic energy and distribution choices.. The model demonstrates flexibility in projecting energy consumption and carbon emissions under diverse scenarios.
What research method was used?: Mechanistic modelling integrating chemical engineering process design, heat exchange principles, and empirical modelling..
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2023 journal from Chemical Engineering Journal.
What should I do differently in my next project?: Use simulation tools to model the energy consumption and carbon footprint of proposed manufacturing and distribution systems, testing various combinations of energy sources, technologies, and logistics before implementation.
What are the limitations?: The model's accuracy is dependent on the quality of input data and the projections for future carbon conversion factors. Specific regional variations in energy infrastructure and consumer demand were not deeply explored.