In order to sustainably use kelp as a fermentation carbon source, we design a process route for kelp growth and kelp fermentation. In order to quantitatively analyze the carbon footprint and carbon utilization rate brought by this process route, we established three models based on the process of kelp farm in the commercial part and the kelp fermentation process in the wet experiment.
We established three models: kelp growth model, fermentation process carbon emission model and carbon utilization model.
In the kelp growth prediction model, we established a mathematical model by considering environmental factors such as seawater temperature and light of kelp. This model can help us understand the growth law of kelp and provide a scientific basis for the breeding site of kelp factory. At the same time, the annual carbon dioxide absorption rate and annual output of kelp are predicted.
In the fermentation process carbon emission assessment model, we modeled the carbon emissions of the two main destinations of kelp: sales and fermentation process based on the life cycle of kelp, and compared the carbon emissions of the two flows. The carbon emissions generated in the kelp fermentation process can be quantified and simulated according to the fermentation conditions, so as to evaluate the carbon emission impact of the kelp fermentation process on the environment.
We also combine the fermentation carbon utilization model of kelp to effectively manage and utilize the carbon emissions in the kelp fermentation process. By optimizing the fermentation conditions, improving the carbon utilization rate, and reducing carbon emissions, we can achieve low carbonization and carbon recycling in the kelp fermentation process.
Taking into account the three models we constructed: kelp growth prediction model, carbon emission model and carbon utilization model, we can provide a mathematical explanation for the cultivation and fermentation of kelp. It is hoped that future research can make more progress in carbon emissions and utilization during kelp growth and fermentation, and provide more sustainable development solutions for ecological environmental protection and resource utilization in the kelp industry.
Figure 1.Overview
Kelp is a perennial large edible algae [1], which is effective in preventing cancer [2]. It is brown and flat, contains spore sacs and mucus cavities, is attached to the seabed rocks, and is adapted to low temperature environments [3]. It is a subarctic algae, endemic to the North Pacific and also distributed in the Atlantic Ocean, mainly in the Northern Hemisphere and to a lesser extent in the Southern Hemisphere. Its growth is regulated by multiple factors such as temperature, light, and nutrients [4]. The northwest Pacific is its main growth area, and species with high economic value are concentrated along the coasts of Russia and Japan. After introduction, it is also widely cultivated in China and South Korea. The diversity of kelp species is closely related to the high latitudes of the two countries, making them the world's major kelp producers [5].
Figure 2.Distribution of kelp farming sites around the world
At present, a lot of research has been carried out on the growth of kelp, mainly focusing on the physiological ecology and population dynamics of the effects of environmental factors such as nutrients [6], temperature [1, 7], and light [8-10] on kelp growth and photosynthesis, but there is a lack of effective models to study and predict the impact of the environment on the growth process and yield of kelp in aquaculture waters. Based on Nanri Island explored in the HP activity as the target site, we explored the impact of changes in temperature and light conditions on kelp growth. According to the experimental data on kelp physiological ecology, environmental parameters for kelp growth, and kelp growth data, we used dynamic simulation methods to establish a mathematical model to simulate and predict kelp growth.
The absorption of nutrients by kelp is not only affected by the surrounding marine environment, but also the way it absorbs and utilizes nutrients will vary under different environmental conditions. Environmental temperature, light, nitrogen and phosphorus nutrients are closely related to the growth rate, debris shedding rate, spore release rate, leaf tip rot rate, nitrogen and phosphorus nutrient absorption rate of kelp [11-14]. This study uses environmental temperature and light as forced functions, and based on the net growth rate of kelp, constructs a kelp growth model, simulates its growth conditions, and studies the effects of these two environmental parameters on kelp growth.
The absorption of nutrients by kelp is not only affected by the surrounding marine environment, but also the way it absorbs and utilizes nutrients will vary under different environmental conditions. Environmental temperature, light, nitrogen and phosphorus nutrients are closely related to the growth rate, debris shedding rate, spore release rate, leaf tip rot rate, nitrogen and phosphorus nutrient absorption rate of kelp [11-14]. This study uses environmental temperature and light as forced functions, and based on the net growth rate of kelp, constructs a kelp growth model, simulates its growth conditions, and studies the effects of these two environmental parameters on kelp growth.
| Symbol | Parameter | Unit | Definition | Parameter source |
|---|---|---|---|---|
| GR0 | 0.10 | d-1 | Initial growthrate | ［15，16］ |
| A | 0.02 | d-1 | Specificgrowth rateparameter | ［15，16］ |
| F | 2 | Dimensionless | Specificgrowth ratetolerance limit | ［15，16］ |
| IOPT | 180 | μmol /( m2·s) | Optimum lightintensity forphotosynthesis | ［17］ |
| β | 3 | Dimensionless | Temperature function parameter | ［18］ |
| TOPT | 13 | °C | Optimum growth temperature | ［19］ |
| Tmax | 25 | °C | Upper limit of temperature ecological radiation | ［20］ |
| Emax | 5×10－5 | d-1 | Kelp loss rate | ［21］ |
| SD | -43 | d | Kelpcultivation starttime (expressed as the first or last day of theyear) | Based on the actualsimulated sea area |
Figure 3.Parameters of the kelp growth function
After establishing the growth model of kelp, we selected Nanri Island in Putian District, Fujian Province as the analysis object and used genetic algorithms to obtain the light and temperature most suitable for kelp growth. Genetic algorithm is a computational method inspired by natural selection and genetics theory, used to solve optimization problems. Genetic algorithm simulates the genetic process in nature and searches for the optimal solution by simulating the evolutionary process.
The basic idea of genetic algorithm is to randomly generate a set of initial solutions in a solution space, and then use biologically based genetic operations (such as selection, crossover and mutation) to continuously evolve these solutions until a satisfactory solution is found or the specified termination condition is reached.
Figure 4.Steps of genetic algorithm
The basic steps of genetic algorithm include:
- Initialize the population: randomly generate a set of initial solutions as the population.
- Fitness evaluation: perform fitness evaluation on each solution to determine its quality.
- Selection: select a part of the solutions as the parents of the next generation population according to the fitness value.
- Crossover: randomly select a pair of parent solutions for crossover operation to generate a new solution as part of the next generation population.
- Mutation: perform mutation operation on the new solution to introduce a certain degree of randomness.
- Replacement: Replace part of the solutions in the current population with the newly generated solutions.
- Repeat steps 2 to 6 until the stopping condition is met.
The growth of kelp is affected by many environmental factors, including water temperature, light, salinity, nutrients, etc. Among them, water temperature is one of the important factors affecting the growth of kelp. Generally speaking, suitable water temperature can promote the growth of kelp, while too high or too low water temperature will inhibit the growth of kelp. Therefore, we establish the growth model of kelp as a dynamic system affected by water temperature, and use genetic algorithms to find the optimal growth law.
First, we defined a function above to evaluate the effect of temperature on the net growth rate of kelp. Then, it is necessary to determine the parameters of the genetic algorithm, such as population size, crossover probability, mutation probability, etc. Next, we can get a population of temperature from the website and evaluate the suitability of each temperature according to the fitness function. Then, the temperature population is iteratively optimized using operations such as selection, crossover, and mutation until the stopping condition is met.
Temperature and temperature forcing function:
$$ft=\frac{2.0\times \left( 1+\beta \right) \times Xt}{Xt^2+2.0\times \beta \cdot Xt+1.0}$$
$$Xt=\frac{T-T_{\max}}{T_{OPT}-T_{\max}}$$
β represents the temperature function parameter, dimensionless;T represents the actual temperature, Tmax represents the upper limit of the temperature ecological radiation, and TOPT represents the optimal growth temperature, all in °C
During the iteration process, the temperature population can be continuously adjusted, and the selection, crossover and mutation operations of the temperature can be guided according to the evaluation results of the fitness function to gradually optimize the temperature population and finally find the optimal temperature for kelp growth. The following image can be obtained:
Figure 5.Growth rate of kelp changing with temperature
From the image, we can get the optimal temperature for kelp growth: 13℃.
Next, we build an optimization model to maximize the growth rate of kelp. The lighting function is as follows:
$$f_i=\frac{I}{I_{OPT}}\times \exp \left( 1-\frac{I}{I_{OPT}} \right) $$
I represents the actual light intensity, and IOPT represents the optimal light intensity for kelp photosynthesis, both in µmol/(m2 ·s).
Substituting the light data of Nanri Island in Fujian Province, we can get the following image:
Figure 6.Growth rate of kelp changing with light intensity
From the image, we can get the optimal light intensity for kelp growth: 38µmol·m2 ·s.
The growth rate of kelp has an important impact on the yield and quality of kelp. In order to better predict the growth rate of kelp, we use the long short-term memory network (LSTM) in the neural network algorithm for time series prediction.
LSTM is a special recurrent neural network (RNN), whose main feature is the ability to remember long-term dependencies and forget unnecessary information. This feature makes LSTM very effective in dealing with time series prediction problems, especially suitable for processing data with long-term dependencies.
The growth rate of kelp is affected by many factors, such as temperature, light, etc. The formula is as follows:
$$ngr=tgr\times \left( 1-resp \right) $$
$$resp=\frac{0.3179\times T^2-6.5728\times T+52.851}{100}$$
$$tgr=gr\cdot fi\cdot ft$$
$$gr=GR0+A\times \exp \left( -\frac{\left( NP+NP_{OPT} \right) ^2}{2\times F^2} \right) $$
GR0 represents the initial growth rate; the unit is d-1; A represents the specific growth rate parameter, the value is equal to the difference between the maximum growth rate and the initial growth rate (GR0), the unit is d-1; NP represents the nitrogen-phosphorus ratio in the aquaculture water, dimensionless; NPOPT represents the optimal nitrogen-phosphorus ratio in the aquaculture water, dimensionless; F represents the specific growth rate tolerance limit, dimensionless.
In order to simplify the calculation, we will not consider the nitrogen-phosphorus ratio factor for the time being. We will take other factors as input variables and the growth rate of kelp as the target variable to build an LSTM model for time series prediction of growth rate.
First, we need to collect the data of kelp growth rate and related influencing factors within a certain period as a training set. Then, preprocess these data, such as normalization, so that they can be better input into the LSTM model. Next, build the LSTM model, determine the parameters such as the number of neurons in the input layer and the output layer, the number of layers in the hidden layer, and train it.
During the training process, the LSTM model will continuously adjust the parameters by learning the relationship between historical data to better fit the time series law of kelp growth rate. After training is completed, the model can be verified using the test set to evaluate the model's predictive performance.
Research shows that the growth rate of kelp is greatly affected by temperature. Therefore, we made a prediction of the future growth rate of kelp based on temperature as shown in the following figure:
Figure 7.RMSE (root mean square error changes with the number of iterations) of the LSMT time series prediction network for temperature
Figure 8.RMSE (root mean square error as a function of iterations) of the illumination LSMT time series prediction network
At the beginning of the iteration, the RMSE is high, and as the number of iterations increases,
the
RMSE gradually
decreases and stabilizes after about 500 iterations. The model continuously optimizes its
prediction
performance
during the training process, making the gap between the prediction results and the actual values
smaller and
smaller.
$$RMSE = \sqrt{\frac{1}{m}\sum_{i=1}^m{\left( y_i - \widehat{y_i} \right) ^2}}$$
$$y_i - \widehat{y_i}$$
is the true value minus the predicted value on the test set.
Figure 9.Temperature forecast for kelp planting in 2024
Figure 10.Light forecast results for kelp planting in 2024
The above figure shows the prediction results of temperature and light during kelp planting in 2024. The horizontal axis is time and the vertical axis is the training result. It is divided into two parts: observation value and prediction value. The training data is the temperature/light from 2021 to 2023.7. The observation value and the prediction value are close most of the time, indicating that the model has a certain grasp of the future trend.
The figure below shows the actual data, with the horizontal axis being time and the vertical axis being the actual data. The actual data fluctuates over time and has a certain periodicity. The prediction model performs well most of the time and can simulate the trend of the actual data well.
According to the optimization results of the genetic algorithm, the optimal light and temperature for kelp growth are: 13℃, 38 µmol·m²·s⁻¹.
According to the collection and processing of the temperature of Nanri Island, we get the average temperature of Nanri Island to be 19.2℃. Therefore, judging from the ocean currents and geographical location of Nanri Island, it can be roughly known that the temperature on the north bank of Nanri Island is lower than that on other banks. Therefore, the kelp production will be higher if a kelp breeding base is established on the north bank of Nanri Island.
Figure 11.Nanri Island, Putian City, Fujian Province. The black area near the shore in the picture is the kelp farming area
$$\frac{Yi_{t1}}{Yi_{t2}}=\frac{\sum_{t1}{ngr}}{\sum_{t2}{ngr}}$$
Among them, $$Yi_{t1}$$ and $$Yi_{t2}$$ represent the kelp production in the t1th and t2th years, and $$\sum_{t1}{ngr}$$ and $$\sum_{t2}{ngr}$$ represent the sum of the kelp growth rates in the t1th and t2th years.
According to the light and temperature conditions in 2022 and the production in 2022, as well as the optimal temperature and light for kelp growth, the ideal production of kelp in Nanri Island is 158,600 tons.
Similarly, using formula (1) and the predicted growth of kelp in 2024, the predicted production of kelp in 2024 is 84,700 tons.
As the challenge of global climate change becomes increasingly severe, reducing greenhouse gas emissions has become a global consensus. As an important carbon reservoir on the earth, the ocean has huge negative emission potential. With the increasing demand for healthy food and seafood and industrial demand, the scale of kelp farming is also expanding. China is the world's largest marine aquaculture country, and the scale of kelp farming is also among the top.
After the kelp is produced, it has multiple flows. However, a large amount of greenhouse gas emissions may be generated during different flows, which has an impact on the environment that cannot be ignored. However, the specific values of the source and sink effect of carbon dioxide in different flows of kelp are still lacking.
In order to study the role of this project in alleviating the climate crisis, we need to analyze the carbon footprint of kelp in different flows. Taking the kelp fermentation process as an example, we propose a set of carbon emission estimation methods suitable for the kelp fermentation process. The method includes steps such as determining the accounting boundary, identifying the main emission sources, calculating various types of emissions, and summarizing the total emissions.
Kelp is an aquatic plant that usually grows in seawater or saltwater environments. The different flows of kelp are mainly divided into kelp seedling cultivation, kelp sea area fence, kelp growth and harvesting aquaculture stage, and fermentation stage and sales stage. In these processes, a certain amount of greenhouse gas emissions may be generated[22].
Kelp seedling cultivation stage: Kelp seedlings are usually obtained through artificial cultivation. This process may consume a certain amount of electricity, water resources, etc., and also generate a certain amount of greenhouse gas emissions.
Kelp sea area fence stage: The next step is to fence the kelp seedlings into the sea area. This process may involve the use of ships, fuel consumption, etc., and also generate a certain amount of greenhouse gas emissions.
Kelp growth stage: Kelp growth requires nutrients and light in the waters. The fertilizers, pesticides used in the aquaculture process, and the electricity consumed will generate greenhouse gas emissions.
Harvest and processing stage: The harvesting and processing/selling of kelp involves the use of machinery and equipment, energy consumption, and also generates a certain amount of greenhouse gas emissions.
Transportation stage: The dried kelp is transported to different places, namely the fermentation factory and the sales site.
Fermentation stage: The kelp is fermented in a series of processes in the fermentation factory.
Preservation stage: The kelp that has not been sold is preserved during the kelp sales process.
Figure 12.Diagram of different kelp flow process
The analysis method process is as follows:
First, it is necessary to collect information on various resources, energy, raw materials, etc. involved in the kelp flow process, including the fertilizers, pesticides, electricity, fuel, etc. used, as well as the corresponding greenhouse gas emissions.
Then analyze and organize the collected data to calculate the greenhouse gas emissions during the kelp flow process. Finally, convert the carbon emissions into carbon footprint indicators, such as carbon emissions per unit area or unit output.
Finally, based on the evaluation results, put forward corresponding environmental protection strategies and suggestions to reduce the carbon footprint during the kelp flow process.
As an important primary producer, kelp flows absorb inorganic carbon, nutrients and other synthetic organic matter in the water through photosynthesis, and their growth process can absorb fixed carbon. This study evaluated the carbon footprint of kelp flows using Sanggou Bay in Rongcheng City, Shandong Province as an example. The production of 1 ton of kelp (wet weight) was recorded as a functional unit, and the entire breeding process was divided into three stages: farming stage, transportation stage, and grow-out stage using the "cradle to gate" life cycle method.
The inventory analysis mainly considers the energy and nutrient inputs during the farming stage, road transportation during the transportation stage, and the energy and aquaculture facility inputs during the grow-out period, as well as the carbon dioxide fixed during the kelp growth process. During the growth process, kelp fixes a large amount of carbon through photosynthesis, part of which is retained in the form of biomass carbon, and the other part is released into the seawater in the form of dissolved organic carbon (DOC) and particulate organic carbon (POC). DOC and POC can be converted into inert dissolved organic carbon (RDOC) or buried on the seabed to form a long-term carbon sink under the action of the microbial carbon pump. Kelp biomass carbon, the formed RDOC and sedimentary buried carbon are regarded as negative emissions of CO2 and recorded as negative values.
We calculated the carbon footprint of the aquacultured kelp in Nanri Island from "cradle to gate" in accordance with the requirements of the life cycle assessment method. The calculation formula is:
$$CF=\sum_{i=1}^n{V_i\times F_i}$$
Where CF is the carbon footprint of cultured kelp (kgCOe), Vi represents the consumption/output of the i resource or energy; Fi represents the emission factor of the i resource or energy.
As shown in Figure 13, the carbon footprint of kelp farming is -95.93 kgCO2e/t, of which the CO2 absorption of kelp is 170.23 kgCO2e/t and the emission is 74.30 kgCO2e/t (2.24% in the nursery stage, 97.76% in the grow-out stage, and only 0.00005% in the transportation stage).
| Life cycle stage | Items | Data | Carbon emission factor | CO2 emissions/kg |
|---|---|---|---|---|
| Breeding stage | Diesel | 0.375kg(1) | 2.17(3) | 0.81 |
| Electric energy | 0.86KWh(1) | 0.997(3) | 0.85 | |
| NaNO3 | 1.1g(1) | 1.63(4) | 2.00×10-3 | |
| KH2PO4 | 0.16g(1) | 1.53(4) | 3.00×10-4 | |
| Transport stage | Transportation distance | 2km(1) | 0.172(4) | 4.00×10-5 |
| Vehicle | 6t(1) | |||
| Transportation volume | 3500 | |||
| Culture Phase | Polyethylene | 115.8kg(2) | 0.6029(5) | 69.70 |
| Diesel | 1.34kg(1) | 2.17(3) | 2.94 | |
| Kelp biomass carbon | 37.1kg | -44/12(6) | -136.00 | |
| Deposition carbon | 6.54kg | -44/12(6) | -23.98 | |
| RODC | 2.79kg | -44/12(6) | -10.25 | |
| Carbon footprint | -95.93 |
Figure 13. Relevant data and carbon footprint calculation results for production of l t kelp
Note:
(1) Data from Rongcheng Xunshan Group;
(2) Data from Liu (2019);
(3) Carbon emission coefficients of diesel and electricity are from IPCC emission factor database;
(4) Carbon emission coefficients of road transportation, KH2PO4 and NaNO3 are from CLCD 0.7 database;
(5) Data from Li (2008);
(6) 44/12 is the relative molecular mass ratio of carbon dioxide to carbon. The unit of carbon emission coefficient of electric energy is kgCO2e/kW·h, and the unit of carbon emission coefficient of highway transportation is kgCO2e/(t·km).
Figure 14. CO2 emissions at each production stage during kelp farming
The vertical axis represents carbon dioxide emissions, and the horizontal axis represents different production stages, including the breeding period, transportation period, and cultivation period. It can be seen from the figure that the carbon dioxide emissions during the cultivation period are positive, about 1.67 kg, indicating that the kelp emits a large amount of carbon dioxide during this period. The carbon dioxide emissions during the transportation period are extremely small and almost negligible, about 1e-35 kg. The carbon dioxide emissions during the cultivation period are negative, about -97 kg, indicating that a large amount of carbon dioxide is absorbed during this stage. In general, in the process of kelp cultivation, in addition to a certain amount of carbon dioxide emissions during the cultivation period, a large amount of carbon dioxide will be absorbed during the breeding period, which helps to offset part of the carbon emissions. Carbon emissions during the transportation period can be ignored.
Figure 15.CO2 emissions during the seedling raising period
There are four sources of carbon emissions during the seedling period. The carbon emissions from electricity are 0.85 kg CO2e/t, accounting for 51.13% of the carbon emissions during the seedling period and 1.14% of the emissions during the entire life cycle; the carbon emissions from diesel during the seedling process are 0.81 kg CO2e/t, accounting for 48.73% of the carbon emissions during the seedling period and 1.09% of the emissions during the entire life cycle; the emissions from the use of two types of fertilizers are 2.00×10-3 and 3.00×10-4 kg CO2e/t, respectively, accounting for 0.14% of the carbon emissions during the seedling period.
Through carbon analysis of the above links, we can evaluate the carbon emissions generated during the entire kelp farming process as follows:
$$p1=0.85+0.81+0.002+0.0003=1.6623 CO_2/kg$$
It indicates the total CO2 emissions during the breeding period, of which diesel and electricity are the main emission sources, each contributing nearly half of the emissions. The part less than 1% is relatively small, referring to the CO2 emissions from the fertilizer inputs of NaNO3 and KH2PO4 during the breeding process.
Transportation stage:
After being harvested, kelp has two directions. One is to go to the fermentation factory for the next fermentation to obtain limonene and other products, and the other is to be transported to the market for sale. The first route is from Nanri Island to Shenzhen Biocreatech Company in Shenzhen. The transportation method is truck transportation. Large trucks rated at 30 tons are used to transport dried kelp. The second route is from Nanri Island to Yiwu City. The transportation method is truck transportation. Large trucks rated at 30 tons are used to transport dried kelp. The transportation route is shown in the figure:
Figure 16.Two transportation road map
The first route is 785 kilometers long, and 50 tons of dried kelp are needed for fermentation, so the carbon emission coefficient of each kilogram of kelp during transportation can be calculated; the second route is 644 kilometers long, and about 300 tons of kelp are sold, so the carbon emission coefficient of each kilogram of kelp during transportation can be calculated:
The truck is a long-distance truck powered by diesel, and the long-distance truck environmental emission factor in the raw material database is directly used for calculation [9]. Therefore, the amount of diesel required to transport 1 kg of kelp is 0.00656 kg. We use GaBi software to model and solve, and can calculate the carbon emissions of 50 tons of kelp and 300 tons of kelp at a distance of 785 km and 644 km respectively:
Figure 17.environmental emissions from the transport of 1 kg of kelp transportation
From the table above, we can conclude that the carbon emissions during transportation are:
The first route:
$$p2=4.16\times 10^{-2}\,\,CO_2/kg$$
$$p2=4.16\times 10^{-2}\,\,CO_2/kg$$ (11) $$p3=1.5\times 10^{-1}\,\,CO_2/kg$$
The grow-out stage is divided into two parts: fermentation and selling.
2.4.1 In fermentation part
The main emission sources in the kelp fermentation process are likely to include fossil fuel combustion (if steam boilers are used), methane emissions from wastewater treatment, and indirect emissions from the electricity used. These emission sources are major contributors to greenhouse gas emissions and therefore need to be carefully identified and quantified.
If steam boilers or other forms of fossil fuel combustion equipment are used in the kelp fermentation process, it is necessary to record the fuel consumption, the average lower calorific value of the fuel and the emission factor. The calculation formula is as follows:
$$E_{CO\_burning}=\sum{_i\text{（}FC_i\times NCV_i\times EF_i\text{）}}$$
Where, $$FC_i$$ is the consumption of the i-th fossil fuel, in tons; $$NCV_i$$ is the average lower calorific value of the i-th fossil fuel, in megajoules/ton; $$EF_i$$ is the carbon dioxide emission factor of the i-th fossil fuel, in tons of carbon dioxide/megajoule.
Based on the total amount of organic matter removed during wastewater treatment, the methane emission factor, and the global warming potential (GWP) of methane, the formula is used:
$$E_{CO_2}^{water}=\left[ \left( TOW-S \right) \times EF-R \right] \times GWP_{CH_4}\times 10^{-3}$$
where TOW is the total organic matter content in the wastewater, in kilograms; S is the amount of organic matter removed, in kilograms; EF is the methane emission factor, in kilograms of methane/kilogram of organic matter; R is the amount of methane recycled, in kilograms; and GWPCH4 is the global warming potential of methane, which is usually taken as 25[24].
The kelp fermentation process uses externally purchased electricity, so the amount of electricity needs to be recorded and the formula used is:
$$E_{CO_2}^{^{electricity}}=AD_{electricity}\times EF_{electricity}$$
Calculate the CO2 emissions related to electricity. The grid supply emission factor AD can be obtained from the local power company, or refer to the relevant data released by the country.
Finally, add up the above emissions to get the total greenhouse gas emissions of the kelp fermentation process. This step is crucial to understand the overall carbon footprint of the entire kelp fermentation process. By summarizing the calculation formula:
$$E_{GHG} = E_{CO_2}^{\mathrm{burning}} + E_{CO_2}^{\mathrm{process}} + E_{CO_2}^{\mathrm{water}} + E_{CO_2}^{\mathrm{electricity}} + E_{CO_2}^{\mathrm{heat}}$$
Substitute the fermentation plant data (data from our sponsor partner Biocreatech):
| Equipment/Operation | Power | Time |
|---|---|---|
| 15 L Fermenter | 1 kW | 24 h |
| 150 L Fermenter | 1.5 kW | 12 h |
| 1500 L Fermenter | 20 kW | 168 h |
| Cooler | 23.36 kW | 168 h |
| Steam boiler | 2 kW | 10 h |
| Sterilization | 4.6 kW | 8 h |
| Purification | 0.4 kW | 600 h |
Figure 18.Power and time of kelp fermented equipment/operation
The total power consumption AD is calculated as 7633 kW·h. According to the "Carbon Dioxide Emission Accounting Method and Data Verification Table", the power supply emission factor ADelectricity in Guangdong area is 0.5912.
According to formula (15), $$E_{CO_2}^{electricity}$$ is 4512.6296 CO2/kg, that is, p4 = 4512.6296 CO2/kg.
By analyzing the carbon in the above links, we can evaluate the carbon emissions generated in the entire kelp farming and fermentation process as follows:
2.4.2 In selling part
The carbon footprint of the kelp produced in different channels of flow and fermentation is calculated below. The flow carbon footprint mainly includes sales and packaging as well as storage during the sales process.
| Equipment | Power | Time |
|---|---|---|
| Refrigeration system | 21 kW | 360 h |
| Lyophilizer | 12.5 kW | 150 h |
| Packer | 1 kW | 96 h |
The total power consumption is 9531 kW·h.
According to the "Carbon Dioxide Emission Calculation Method and Data Verification Table," the power supply emission factor AD electricity in Guangdong is 0.5912.
According to formula (15), \(E_{CO_2}^{electricity}\) is 5634.7272 CO2/kg, that is, p6=5634.7272 CO2/kg.
By conducting carbon analysis on the above links, we can evaluate the carbon emissions generated in the entire kelp sales process as follows:
$$p7=p1+p3+p6=0.15+1.6623+5634.7272=5636 CO_2/kg$$
Through data collection and detailed analysis of each stage of kelp farming, fermentation, and sales, we can see that the sales process will generate more carbon emissions than the fermentation process by comparing the carbon emission data of the fermentation process and the sales process:
$$p7-p5=5636.5395-4514.3335=1122.206 CO_2/kg$$
Therefore, we have come to an important conclusion: fermented kelp has lower carbon emissions than directly sold kelp.
In terms of carbon emissions, we also have the following suggestions:
- Kelp farming stage: In this stage, the main considerations include kelp seedling preparation, planting, growth management, and harvesting processes. These activities will generate certain greenhouse gas emissions, such as fuel used for transportation, electricity required for equipment operation, and fertilizer use.
- Fermentation processing stage: Fermentation processing is a process of converting kelp into higher value-added products. This stage involves using chassis cell brewer's yeast to convert organic matter in kelp into limonene. Compared with kelp sold directly, fermented kelp increases the utilization value of kelp.
- Selling stage: In the case of direct sales, since kelp needs to be kept fresh, a large amount of cooling facilities are required during transportation and storage, which increases energy consumption. In contrast, fermented kelp requires lower cooling and packaging costs due to its longer shelf life and smaller volume, thereby reducing the overall carbon footprint.
Fermented kelp shows lower carbon emission characteristics in all links of breeding, processing, and sales. This is mainly because the fermentation process reduces the cost of subsequent logistics and storage and increases the added value of the product. Therefore, when seeking sustainable development strategies, fermented kelp is a very promising option.
Kelp has a strong cell wall, making it difficult to degrade, and it contains a large amount of polysaccharides, especially alginate, kelp starch, and mannitol. Among these, alginate cannot currently be used, and kelp starch needs to be broken down into monosaccharides before it can be utilized. Therefore, a special process is required to prepare it for use. After treatment, the nutrients in the kelp will be fully released and subsequently used for fermentation to produce a variety of high-value-added bio-based products, such as limonene.
In the wet experiment section, we constructed a kelp processing process and utilized a kelp culture medium for fermentation to produce limonene. To optimize this process and enhance its economic benefits, we developed a carbon utilization model. This model describes the conversion efficiency of carbon elements during the fermentation process through mathematical formulas, helping to determine the optimal fermentation conditions to maximize the yield of the target product, limonene.
The application of this model also aids in evaluating the environmental impact of the fermentation process, such as reducing carbon dioxide emissions and minimizing energy consumption. By establishing a carbon utilization model, we can quantify and analyze the efficiency of fermenting kelp degradation products to produce byproducts such as limonene, while also promoting the development of green chemistry and biotechnology, aligning with current sustainable development goals.
The chemical composition of kelp is primarily: 57% carbohydrates, 8.2% protein, 0.1% fat, 9.8% crude fiber, and various inorganic elements including calcium, iron, manganese, zinc, boron, iodine, selenium, potassium, and vitamins.
Since we are primarily concerned with the content of the main substances, we measured the levels of alginate, mannitol, and glucose in the wet experiment.
In the wet experiment, we determined that the mannitol content in the treated kelp hydrolysate was 40.80 g/L, the glucose content was 3.88 g/L, and the alginate content was 20.78 g/L.
To view Experiment to learn more.
Figure 20.Components of treated kelp media
| Symbol | Description | Unit |
|---|---|---|
| η | Carbon utilization rate of the fermentation process | / |
| Xi | Number of carbon atoms in the chemical formula of the i-th fermentation product | / |
| yi | Number of hydrogen atoms in the chemical formula of the i-th fermentation product | / |
| Zi | Number of oxygen atoms in the chemical formula of the i-th fermentation product | / |
| mi | Yield of the i-th fermentation product | g |
| m0 | Initial kelp mass | g |
| β | Percentage of carbon mass in kelp | / |
Atomic weight of carbon: $$M_C = 12.01 \, \text{g/mol}$$
Atomic weight of hydrogen: $$M_H = 1.008 \, \text{g/mol}$$
Atomic weight of oxygen: $$M_O = 16.00 \, \text{g/mol}$$
The atomic weight of the compound:
$$M_i=\text{（}x_i\times M_C\text{）}+\text{（}y_i\times M_H\text{）}+\text{（}z_i\times M_O\text{）}$$
Carbon content in the first metabolite:
$$\alpha _i=\text{（}\frac{x_i\times M_C}{M_i}\text{）}\times 100\%$$
Total carbon content in fermentation products:
$$\alpha =\sum_i{m_i\alpha _i}$$
Substituting into (15), (16), (17), we obtain:
$$\alpha =\sum_i{m_i\frac{x_i\times M_C}{\text{（}x_i\times M_C\text{）}+\text{（}y_i\times M_H\text{）}+\text{（}z_i\times M_O\text{）}}}$$
Carbon content in fermentation raw materials:
$$\alpha _0=m_0\beta $$
Carbon utilization:
$$\eta =\frac{\alpha}{\alpha _0}$$
Substituting into (18) and (19) we get:
$$\eta =\frac{\sum_i{m_i\frac{x_i\times M_C}{\text{（}x_i\times M_C\text{）}+\text{（}y_i\times M_H\text{）}+\text{（}z_i\times M_O\text{）}}}}{m_0\beta}$$
In the fermentation experiment, 20 grams of kelp were used to make 200 mL of kelp culture medium, in which the concentration of mannitol was 18 g/L, the concentration of glucose was 2 g/L, and the concentration of alginate was 40 g/L. In this culture medium, our engineered yeast AG05 was fermented for 144 hours in batch fermentation, and finally, 51.02 mg/L of limonene was obtained. Based on the experimental data, substituting into (21), we get:
Although this calculated result shows that there is still room for improvement in conversion efficiency, it verifies the feasibility of producing high-value-added chemicals from kelp degradation products. However, this is a process from 0 to 1, and our strains still have a lot of room for improvement. At the same time, if we can recycle or degrade the alginate in the fermentation waste liquid to recover AOS (brown algae oligosaccharides) in the future, it will greatly improve the carbon utilization rate.
This model describes the conversion efficiency of carbon elements during the fermentation process through mathematical formulas, helping to determine the carbon conversion rate corresponding to different limonene production under different fermentation conditions, in order to measure the environmental protection ability of our strain.
In order to sustainably use kelp as a fermentation carbon source, we established three models: kelp growth model, fermentation process carbon emission model, and carbon utilization model. These three models quantified our commercial idea of opening a kelp farm and the carbon footprint of the kelp fermentation process. Our modeling not only helped us better understand the problem, but also filled the gap in the research on the carbon footprint of microbial transformation of kelp substances.