BL40A2010 Introduction to IoT-Based Systems

Final Work, 16.03.2024

Author: Zhuo Song

Introduction

As sustainability continues to evolve, the environmental impact of plastic food packaging and its life cycle assessment have also been studied. IoT is becoming a key method to reshape how we monitor and manage CO2 emissions across industries. The intricate relationship between food, package size, packaging material CO2 coefficient and food CO2 coefficient presents complex challenges in reducing carbon footprint. This report uses diagrams from the same technology as the course for analysis. The Internet of Things optimizes packaging size and materials based on data, minimizing waste and reducing carbon emissions. By analyzing the carbon dioxide coefficient of various packaging materials, materials with a lower environmental footprint are selected. At the same time, the study can be analyzed using node visualization, which is also an important method.

Packaging and food waste produce CO2 as a system

CO2 emissions from packaging and food waste are a key issue. The impact of packaging and waste on food CO2 emissions is multifaceted and involves a complex interplay of production practices, material selection, consumer behavior and external environmental factors. Here we explore these dimensions under three main categories: C1: Conditions of production Resource extraction and material production: The production of packaging materials generates significant CO2 emissions. Different materials have different levels of carbon footprint. Energy use in food production and packaging: The energy required for food production and manufacturing of packaging materials is a significant source of CO2 emissions. C2: Conditions of reproduction Packaging material selection: The choice of packaging materials affects the overall environmental impact. Optimizing packaging size and using materials with a lower CO2 coefficient can reduce the carbon footprint. C3: External conditions Technological innovation and sustainable alternatives: Technological advances that provide sustainable packaging alternatives can reduce the carbon footprint of food packaging.

Networking and communication paths

! pip install --upgrade networkx import networkx as nx #https://networkx.github.io/ import matplotlib.pyplot as plt #https://matplotlib.org/ import scipy as sp import numpy as np import networkx as nx G = nx.Graph() G.add_node(1) G.add_nodes_from([2,3]) G.add_nodes_from([3,4]) G.add_nodes_from([4,5]) G.add_nodes_from([5,6]) G.add_nodes_from([6,7]) G.add_nodes_from([7,8]) G.add_nodes_from([8,9]) G.add_nodes_from([9,10]) G.add_edges_from([(1,2),(1,3),(2,4),(2,5),(2,6),(2,7),(4,5),(7,8),(3,9),(3,10)]) nx.draw_networkx(G, node_color='black', font_color='white', edge_color='black', font_weight='bold', node_size=700) plt.axis('off') plt.show()

Run to view results

Let's illustrate the entire system using nodes. 1. Carbon dioxide emissions, 2. Food carbon dioxide emissions, 3. Packaging carbon dioxide emissions, 4. Food size, 5. Food weight, 6. Food carbon dioxide equivalent, 7. Storage days, 8. Preservation coefficient of each food, 9 . Packaging material weight, 10. Packaging CO2 equivalent In this node diagram we can see how material and packaging variables affect total CO2 emissions. They first produce food CO2 emissions and packaging CO2 emissions separately, and then both together produce total CO2 emissions. There are only 10 nodes and 10 links here. If we examine this system in more detail, there may be many more nodes. The node graph is a many-to-one communication network. Node 1 is the main node to which all other nodes pass data. It's a fairly complex system with a lot of variables.

Comparison between different datas

import matplotlib.pyplot as plt import numpy as np material_co2_eq1 = 4.5 # CO2-equivalency of the packaging material food_co2_eq = 0.6 # CO2-equivalency of the food packaging_requirements = { 1: 100, 2: 150, 3: 200, 4: 250, 6: 300, 8: 400 } co2_emissions_1= {} for portion, packaging_weight in packaging_requirements.items(): # Calculate CO2 emissions from packaging packaging_co2 = packaging_weight * material_co2_eq1 # Assuming each portion is 1 kg of food, calculate food CO2 emissions food_co2 = food_co2_eq # Adjust this if portion size affects food weight # Total CO2 emissions for the portion co2_emissions_1[portion] = packaging_co2 + food_co2 co2_emissions_1 portion_sizes_1= list(co2_emissions_1.keys()) emissions_1= list(co2_emissions_1.values()) material_co2_eq2 = 3.3 co2_emissions_2 = {} for portion, packaging_weight in packaging_requirements.items(): # Calculate CO2 emissions from packaging packaging_co2_2 = packaging_weight * material_co2_eq2 # Assuming each portion is 1 kg of food, calculate food CO2 emissions food_co2 = food_co2_eq # Adjust this if portion size affects food weight # Total CO2 emissions for the portion co2_emissions_2[portion] = packaging_co2_2 + food_co2 co2_emissions_2 portion_sizes_2 = list(co2_emissions_2.keys()) emissions_2 = list(co2_emissions_2.values()) material_co2_eq3 = 3 co2_emissions_3 = {} for portion, packaging_weight in packaging_requirements.items(): # Calculate CO2 emissions from packaging packaging_co2_3 = packaging_weight * material_co2_eq3 # Assuming each portion is 1 kg of food, calculate food CO2 emissions food_co2 = food_co2_eq # Adjust this if portion size affects food weight # Total CO2 emissions for the portion co2_emissions_3[portion] = packaging_co2_3 + food_co2 co2_emissions_3 portion_sizes_3 = list(co2_emissions_3.keys()) emissions_3 = list(co2_emissions_3.values()) material_co2_eq4 = 2.2 co2_emissions_4 = {} for portion, packaging_weight in packaging_requirements.items(): # Calculate CO2 emissions from packaging packaging_co2_4 = packaging_weight * material_co2_eq4 # Assuming each portion is 1 kg of food, calculate food CO2 emissions food_co2 = food_co2_eq # Adjust this if portion size affects food weight # Total CO2 emissions for the portion co2_emissions_4[portion] = packaging_co2_4 + food_co2 co2_emissions_4 portion_sizes_4 = list(co2_emissions_4.keys()) emissions_4 = list(co2_emissions_4.values()) plt.figure(figsize=(10, 4)) # Calculate the width for each bar and the positions bar_width = 0.2 # Adjusting the positions for each set of bars positions_1 = np.arange(len(portion_sizes_1)) positions_2 = [x + bar_width for x in positions_1] positions_3 = [x + bar_width for x in positions_2] positions_4 = [x + bar_width for x in positions_3] # Plotting each set of bars plt.bar(positions_1, emissions_1, width=bar_width, label='Material 1 (CO2 eq: 4.5)', color='blue') plt.bar(positions_2, emissions_2, width=bar_width, label='Material 2 (CO2 eq: 3.3)', color='green') plt.bar(positions_3, emissions_3, width=bar_width, label='Material 3 (CO2 eq: 3.0)', color='red') plt.bar(positions_4, emissions_4, width=bar_width, label='Material 4 (CO2 eq: 2.2)', color='yellow') plt.xlabel('Portion Size') plt.ylabel('CO2 Emissions (g)') plt.title('CO2 Emissions by Material and Food') plt.xticks(portion_sizes_1, portion_sizes_1) # Adjust tick positions to the middle of the grouped bars plt.legend() plt.grid(axis='y', linestyle='--') plt.show()

Run to view results

The original data mentioned Portions/day: 3, Days to keep: 5. Multiplying the two we get 15, which means in this report the number of experiments is larger, the maximum package is huge compared to the original data, and here we don't need to consider food waste. The target storage days of food under the four materials can be obtained as Material 1: 5 days*5=25 days Material 2: 5 days*2=10 days Material 3: 5 days*3=15 days Material 4: 5 days*2=10 days In real life, spoiled food increases emissions. Based on the preservation coefficients of different materials, the target preservation days of food under different packaging materials are calculated to be greater than the 5 days given by the initial data.

Conclusion

Compare data by images reflecting different materials of the same size. Therefore, when comparing the CO2 emissions of each package for the same package size, the difference in CO2 emissions can be visually seen through the image. The calculation results are more accurate and the image conclusions are more intuitive. Therefore, the impact of food deterioration on CO2 emissions is not considered. When the dimensions are the same, the lower the carbon dioxide coefficient of the material, the less carbon dioxide emissions. When the material carbon dioxide coefficient is the same, the larger the size, the higher the carbon dioxide emissions. In actual applications, the situation varies. In this experiment, the CO2 equivalent values for different materials and packaging sizes were determined. Since the number of days and retention factor for a specific value are fixed, the data results in the application will be affected by the above factors. Of course, consumer preferences, packaging costs, corporate profits, etc. are also highly influential factors.

Reference

Bishop, G.; Styles, D.; Lens, P.N.L. Environmental performance of bioplastic packaging on fresh food produce: A consequential life cycle assessment. J. Clean. Prod. 2021, 317, 128377.

Data Sources （4，000825832）https://moodle.lut.fi/mod/lti/view.php?id=957063

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