The comparison of algae production systems based on energy consumption and economic analysis: The application of data envelopment analysis
ناصر کاظمی
1
(
گروه مهندسی بیوسیستم، واحد تاکستان، دانشگاه آزاد اسلامی، تاکستان، ایران
)
محمد غلامی پرشکوهی
2
(
دانشیار گروه آموزشی مکانیک بیوسیستم، واحد تاکستان، دانشگاه آزاد اسلامی، تاکستان، ایران
)
احمد محمدی
3
(
گروه مهندسی بیوسیستم، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران
)
داود محمد زمانی
4
(
گروه مهندسی بیوسیستم، واحد تاکستان، دانشگاه آزاد اسلامی، تاکستان، ایران
)
الکلمات المفتاحية: Data envelopment analysis, Economic, energy, Microalgae,
ملخص المقالة :
This paper aims to examine energy use efficiency and economic analysis in different microalgae production systems (open space and greenhouse method) using Data Envelopment Analysis (DEA) method. The data gathered from the laboratory of Islamic Azad University, Arak branch was a place to conduct microalgae production experiments in different systems. The number of samples in each system production of microalgae was estimated to equal 20. The energy result showed that the average total input energy was 15920.40 and 17691.60 MJ kg-1 in the open space and greenhouse conditions, respectively. Also, the energy ratio for the open space and greenhouse conditions was estimated at 0.89 and 0.80, and the energy productivity index at 0.06 and 0.02 kg MJ−1, respectively. According to the economic analysis, the net return of the open space and greenhouse methods were 204376.59 and 269276.06 $ kg-1, respectively. The economic productivity of the open space and greenhouse cultivation methods were determined to be 0.17 and 0.16 kg $−1, respectively. The result of DEA demonstrated that the total optimized consumption of energy in the open space method was 14476.93 MJ kg-1. About 16355.21 MJ kg-1 was saved in the greenhouse method (7.55%) compared to the current cultivation conditions by converting inefficient to efficient cultivation. According to the investigations, it was found that open space cultivation consumes less energy and is more economical than greenhouse cultivation. As a result, promoting microalgae cultivation outdoors is preferable to greenhouse cultivation.
The comparison of algae production systems based on energy consumption and economic analysis: The application of data envelopment analysis
Abstract
This paper aims to examine energy use efficiency and economic analysis in different microalgae production systems (open space and greenhouse method) using Data Envelopment Analysis (DEA) method. The data gathered from laboratory of Islamic Azad University, Arak branch was a place to conduct microalgae production experiments in different systems. The number of sample in each system production of microalgae it was estimated equal to 20. The energy result showed that the average total input energy was 15920.40 and 17691.60 MJ kg-1 in the open space and greenhouse conditions, respectively. Also, the energy ratio for the open space and greenhouse conditions was estimated at 0.89 and 0.80, and the energy productivity index at 0.06 and 0.02 kg MJ−1, respectively. According to the economic analysis, the net return of the open space and greenhouse methods were 204376.59 and 269276.06 $ kg-1, respectively. The economic productivity of the open space and greenhouse cultivation methods were determined to be 0.17 and 0.16 kg $−1, respectively. The result of DEA demonstrated that the total optimized consumption of energy in the open space method was 14476.93 MJ kg-1. About 16355.21 MJ kg-1 was saved in the greenhouse method (7.55%) compared to the current cultivation conditions by converting inefficient to efficient cultivation. According to the investigations, it was found that open space cultivation consumes less energy and is more economical than greenhouse cultivation, so as a result, promoting microalgae cultivation outdoors is more preferable than greenhouse cultivation.
Keywords: Energy, Economic, Data envelopment analysis, Microalgae
1. Introduction
The biggest challenge in the world is the supply of energy for various applications. Energy consumption is divided into four economic sectors: residential, commercial, transportation, and industrial. From heating and cooling homes to lighting office buildings, driving cars, carrying goods, and manufacturing products, we rely in our daily lives on energy for all functions (Luo et al., 2015). At present, the energy demand is supplied through fossil fuels. However, limited fossil fuel resources, environmental issues, increasing population growth, and concerns about increasing energy demand have forced many countries to use renewable energy (Abbasi and Abbasi, 2012).
Land and water resources are often the key limiting factors for the large-scale cultivation of microalgae. In developed areas, land resources are always limited, so it is difficult to find enough area for large-scale cultivation of microalgae (Jiang et al., 2021). Microalga are among the first and simplest organisms with chlorophyll, which convert inorganic into organic matter through photosynthesis, generally have a high growth rate, and can produce a large amount of oil (Brennan and Owende, 2010).
Data Envelopment Analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontier (Kuosmanen and Johnson, 2010). DEA is often used to evaluate production boundaries in research on the process and to experimentally assess the productive efficiency of decision-making units (DMUs) (Geng et al., 2019). It is, indeed, a powerful non-parametric tool to address production economics and operation management, which can benchmark the efficiency of service and construction operations. For the benchmarking purpose, efficient DMUs determined by DEA can furnish the best-practice boundary partly, yet not able to constitute a production boundary (Akdeniz et al., 2010). Non-parametric techniques have the advantage of not prescribing a specific functional form for the boundary, yet they do not provide a general relationship between outputs and inputs. Parametric techniques are also available for the evaluation of production boundaries (Hasilová and Vališ, 2022). Yet, it entails prescribing a specific function between outputs and inputs. A hybrid method to take advantage of each method can also be developed (Kaab et al., 2019a). Therefore, DEA method it has a high potential for optimization in this study. DEA method is a non-parametric, linear programming approach to the estimation of efficiency. The technique does not require any pre-described structural relationship between the inputs and resultant outputs. It can also accommodate multiple outputs in the analysis (Mousavi-Avval et al., 2011).
In recent years, many studies have been conducted in the fields of energy, economy, and environment in which DEA has been used for optimization. For example, Castro and von Zuben (2010) proposed a multi-objective multi-period mixed integer linear programming (MILP) model for the biodiesel supply chain considering the sustainability aspects. The model sought to minimize total cost, greenhouse emissions, and reduction of foodstuff accessibility due to crop substitution for biofuel production.
DEA is an efficient method for optimizing the location of facilities and its merits in measuring the relative efficiency of DMUs have been proven (Velasquez, 2013). Bairamzadeh et al. (2016) presented an MILP model to minimize the total costs of the bioethanol supply chain produced from lignocellulosic biomass raw materials. In their proposed model, such decisions as determining the location, capacity, and technology of biorefineries are taken. Mohseni et al. (2016) identified the most suitable locations for establishing biodiesel production facilities using GIS and the analytic hierarchy process (AHP) at the first stage. In the second stage, they proposed an MILP model for designing and planning microalgae biodiesel supply chain. The environmental objective functions considered in most biofuel supply chain models seek to minimize greenhouse emissions. Han and Kim (2019) proposed a multi-period MILP model in which the objective function was profit maximization. Their model considered different renewable energy resources (wind, sun, and biomass).
Most studies on microalgae biofuel supply chains have only considered the economic aspect with no attention to its sustainability. Arabi et al. (2019) designed a multi-period and non-deterministic supply chain concerning biobutanol production from microalgae biomass raw material to minimize network costs. In their model, such decisions as the location of cultivation, drying, hydrolysis, and workstations in biobutanol refineries and biorefineries are taken. Moreover, in their proposed model, the DEA method was used to rank the locations and reduce computational complexity, but no sustainability aspect was considered in ranking the locations. Designing a biofuel supply chain network may help development and environmental protection, but most studies conducted in this field focus on the economic aspect of the supply chain. Gumte and Mitra (2019) proposed an optimized supply chain network for producing bioethanol from lignocellulosic biomass raw material. Their proposed model seeks to maximize net present value (NPV). Their findings demonstrated that production cost is the biggest cost followed by importation, transportation, infrastructure, and inventory costs. By considering agricultural wastes and forestry residues as biomass raw materials, Razm et al. (2019) proposed a multi-period triple-objective MILP model aimed to find a suitable location for the establishment of new biorefineries and shutting down the existing refineries, if necessary. The economic, environmental, and social objectives considered in their article included maximizing profit excluding tax, maximum greenhouse emission saving, and achieving maximum social profit, respectively. By adopting the DEA approach, the candidate locations are ranked in terms of their efficiency to reduce computational complications and select suitable locations (Mobarezkhoo et al., 2022).
Various studies have addressed microalgae biodiesel fuel, but no research has ever used a combination of the mentioned methods to obtain a wide perspective on commercial microalgae production. This research has determined and optimized energy consumption and conducted economic analysis on microalgae production in different systems to reach the consumption pattern provided by DEA, which introduces an efficient consumption pattern by determining the relative efficiency of the DMUs. The consumption provided by the optimization replaces the current consumption pattern. Finally, by providing solutions and recommendations to improve the efficiency of energy consumption, it greatly helps planning for commercial microalgae production in evaluating energy-economic of algae production, the reviewed items including:
Ø Assessment of energy use and economic analysis in different methods of microalgae cultivation;
Ø Evaluation of energy use efficiency and determination of the inadequacy of energy use in different methods of microalgae cultivation by using DEA to mitigate energy;
Ø Analysis of economic indices in different methods of microalgae cultivation;
Ø Comparison of energy uses and economics in different methods of microalgae cultivation.
2. Materials and Methods
2.1. Location of case study
Microalgae production experiments in different systems were conducted in the laboratory of Islamic Azad University, Arak Branch. To collect information on the type and amount of consumption of inputs and outputs, the number of samples was determined by Eq. 1 (Cochran, 1977). The number of samples in each microalgae production system (open space and greenhouse methods) was estimated at 20.
| (1) |
where N is the population size, z is the reliability coefficient, p is the estimated proportion of an attribute that is present in the population, q is 1-p, and d is the allowed error ratio deviation from the average population.
Algae cultivation greenhouses can be deployed in such places as the roof, yard, basement, room, or greenhouse at a low or medium level. So, not a lot of money is required, and seeds and basic equipment can be procured with little capital. In addition to the place, creating suitable environmental conditions is considered one of the necessities of raising this valuable creature.
Since exposure to sunlight is vital for the process of photosynthesis in algae, conditions must be provided to ensure its light. Also, one of the factors involved in the growth and cultivation of algae is the exposure to a balanced temperature, which is a maximum of 40°C and a minimum of 20°C, and any temperature fluctuations would cease their growth. For example, if the temperature exceeds 40°C, the algae will be killed, or if it drops below 20°C, they will stop growing. Also, the cultivation of microalgae in open space means that the surface of algae cultivation is exposed to the external environment (Malyan et al., 2021).
2.2. Energy use pattern
Today, energy is not only an important component in the development of societies but also an essential element for the development and prosperity of a country. Energy is one of the most important and vital data in people's lives and a basic need for sustained economic development, social welfare, the improvement of the quality of life, and the security of society (Oyedepo, 2012). Energy is the capacity to do work. Although energy is found in different forms, all forms have the capacity to do work. The energy content of each of the inputs and outputs indicates the energy equivalent (Hosseinzadeh-Bandbafha et al., 2018b). Microalgae production inputs include human labor, machinery, natural gas, water, and electricity. Data related to the inputs and outputs of microalgae were collected with a questionnaire filled out through interviews. In the next step, the functional unit was calculated per 1000 kg of microalgae production. Due to the different inputs and outputs of cultivation with different units, comparisons are difficult in these conditions. Figure 1 shows the system boundary in different methods of microalgae cultivation. As a result, all inputs and outputs were converted into energy equivalents through special coefficients. The energy equivalent of each of the inputs is reported in Table 1.
|
Fig. 1. System boundary for different methods of microalgae cultivation. |
Table 1 Energy conversion coefficients used for energy inputs for different microalgae production. | ||
Inputs and outputs (Unit) | Energy equivalent (MJ Unit-1) | References |
A. Inputs |
|
|
1. Human labor (h) | 1.96 | (Bakhtiari et al., 2015) |
2. Machinery and equipment (kg) | 9.00 | (Hatirli et al., 2005) |
3. Electricity (kWh) | 12.00 | (Bakhtiari et al., 2015) |
4. Water (m3) | 1.20 | (Kaab et al., 2019b) |
5. Natural gas (m3) | 49.5 | (Hatirli et al., 2005) |
B. Output |
|
|
1. Microalgae (kg) | 14.20 | (Bahadar and Bilal Khan, 2013) |
Energy indicators such as energy ratio, energy productivity, specific energy, and net energy efficiency were calculated for each microalgae cultivation system (open space and greenhouse methods) in this study. Energy efficiency is the ratio of energy input to energy output of the system (Brentrup et al., 2001). Energy productivity refers to the amount of production of goods and services per unit of energy consumption (Yang et al., 2022). Specific energy is the amount of energy consumption per unit of production of goods and services (Hosseinzadeh-Bandbafha et al., 2018a). The net energy gain is the difference between the total amount of energy output and the input energy (Eq. 3-6).
| (3) | |||||
| (4) | |||||
| (5) | |||||
| (6) | |||||
| (7) | |||||
| (8) | |||||
| (9) | |||||
Maximize | (10) |
Subjected to: | |
|
where yrj and xij denote the output and input values of yr and xi for DMUj, respectively, vi (i = 1, 2, …, m) defines the input weight of xi, ur (r = 1, 2, …, s) defines the output weight of yr, n is the number of DMUs, s and m define input and output numbers, respectively, and θ defines DMU’s TE score. It is expressed in matrix-vector dual linear programming format (Kaab et al., 2019b):
Minimum: θc | (11) |
Subjected to : | |
Yλ ≥ y0 | |
Xλ- θc xo = 0 | |
λ ≥ 0 |
where θc denotes the DMU’s TE score, λ is the n × 1 weight orientation, X denotes an m × n input matrix, Y denotes an s × n output matrix, xo is the m × 1 vector of principal input values employed by the oth DMU, and yo denotes an s × 1 vector of principal output values. Eq. (10) is under the CCR input particle pattern. PTE is equivalent to TE under the BCC model. The BCC model is employed with the addition of a constraint, namely, λ = 1, in the CCR model under double linear programming format in Eq. (11).
In Eq. (12), SE represents the additional efficiency potential for attaining an optimized DMU size. Besides, it denotes the efficiency of the DMU’s size on productivity. The following shows the relationship among PTE, SE, and TE:
| (12) |
In this study, efficiency scores are expressed within a cross-efficiency matrix. The jth column and ith row denote performance scores of the jth microalgae production environment by applying the ideal weights of the ith microalgae production environment, as computed by the CCR model. An effective microalgae production environment is determined based on its average score, which is obtained from each column of the mutual productivity matrix.
3. Results and Discussion
3.1. Input-output energy
Table 2 shows the energy use of various inputs used in different methods of microalgae cultivation. The average of the total input energy was reported in the open space and greenhouse methods to be 15920.40 and 17691.60 MJ kg-1, respectively. The energy input in the greenhouse method was more than that in the open space method of microalgae cultivation. The output energy of the open space and greenhouse methods were both 14200 MJ kg-1. Natural gas, which was the highest energy input in both methods, amounted to 12870 and 17691.60 MJ kg-1 in the open space and greenhouse methods, respectively. The contribution of each of the energy inputs in different methods of microalgae cultivation is shown in Fig. 2. The results show that natural gas in both methods is responsible for about 80% of the total input energy. The depth of open systems is between 10-50 cm, and they can be made with concrete or with a plastic cover of polyethylene or PVC (Pragya and et al., 2013). The closed systems include closed circuits that provide suitable conditions for the growth of microalgae with higher performance. Compared to open pools, these systems increase photosynthetic efficiency and biomass production. However, there are major problems for closed systems, including high initial costs and difficult maintenance conditions, which result in higher energy consumption (Tan et al., 2020). More equipment in the greenhouse production method leads to more human labor hours, and this is followed by more gas consumption. Maximum production should be achieved by controlling the conditions in the greenhouse.
Table 2 Energy consumption of various inputs used in different methods of microalgae cultivation. | |||||
Total energy equivalent (MJ kg-1) | Quantity | Items | |||
Greenhouse | Open space | Greenhouse | Open space | ||
|
|
|
| A. Inputs | |
411.60 | 470.40 | 210.00 | 240.00 | 1. Human labor | |
1170.00 | 1080.00 | 130.00 | 120.00 | 2. Machinery and equipment | |
360.00 | 300.00 | 30.00 | 25.00 | 3. Electricity | |
900.00 | 1200.00 | 750.00 | 1000.00 | 4. Water | |
14850.00 | 12870.00 | 300.00 | 260.00 | 5. Natural gas | |
17691.60 | 15920.40 | - | - | The total energy input (MJ) | |
- | - | - | - | B. Output | |
14200.00 | 14200.00 | 1000.00 | 1000.00 | 1. Microalgae | |
|
|
Fig. 2. Shares of energy sources in different microalgae cultivation methods. |
Table 3 demonstrated the energy indices in different methods of microalgae cultivation. Estimation of the energy ratio of the open space and greenhouse methods were found to have energy ratios of 0.89 and 0.80, productivity energy indices of 0.06 and 0.02 kg MJ−1, and specific energy of 15.92 and 17.69 MJ kg-1, respectively. The net energy gain was reported to be -1720.40 and -3491.60 MJ for them, respectively. Khoo et al. (2013) an investigation of the potential to efficiently convert lipid-depleted residual microalgae biomass using thermochemical (gasification at 850 °C, pyrolysis at 550 °C, and torrefaction at 300 °C) processes to produce bioenergy derivatives was made. Energy indicators are established to account for the amount of energy inputs that have to be supplied to the system in order to gain 1 MJ of bio-energy output. The experimental results showed the lowest results of Net Energy Balances (NEB) to be 0.57 MJ/MJ bio-oil via pyrolysis, and highest, 6.48 MJ/MJ for gas derived via torrefaction. With the complete life cycle process chain factored in, the energy balances of NEBLCA increased to 1.67 MJ/MJ (bio-oil) and 7.01 MJ/MJ (gas). Energy efficiencies and the life cycle CO2 emissions were also calculated.
Table 3 Energy indices of different microalgae cultivation methods. | ||
Items | Open space | Greenhouse |
Energy use efficiency (ratio) | 0.89 | 0.80 |
Energy productivity (kg MJ−1) | 0.06 | 0.02 |
Specific energy (MJ kg-1) | 15.92 | 17.69 |
Net energy gain (MJ) | -1720.40 | -3491.60 |
3.2. Economic assessment
Table 4 shows the economic indices in different methods of microalgae cultivation. As a result, the net return of the open space and greenhouse methods is 204376.59 and 269276.06 ($ kg–1), respectively. Based on high income and low cost, the ratio of benefit to cost is significant in the greenhouse method. The productivity of the open space and greenhouse methods was reported to be 0.17 and 0.16 kg $−1, respectively. Jonker and Faaij (2013) a limitation growth model is developed, which determines the productivity of micro-algae for different climate profiles. Total direct and indirect energy consumption ratios for the production of heat, fuels and electricity derived from micro-algae are calculated. Overall direct energy consumption ratio for raceway ponds is 0.06 for the optimal case, indirect energy consumption ratio for that case is 0.74. Direct energy consumption ratio in horizontal tubular systems is 0.32 for the optimal case, indirect energy consumption ratio for that case is 117. The implementation of different improvement options could reduce the indirect energy consumption ratio by fifty percent for both raceway ponds and horizontal tubular systems in the optimistic scenario. Prominent elements of the energy consumption ratios are carbon dioxide supply for raceway ponds and circulation power consumption for horizontal tubular systems. The lower end of fuel production cost calculated for raceway ponds is 136 €2010/GJ and 153 €2010/GJ for horizontal tubular systems (non-renewable gasoline and diesel is about 5–20 €/GJ). Considering possible improvement options overall bio-energy production costs could be reduced by one-fourth. Current results suggest that micro-algae cultivation is not suitable for dedicated bio-energy production in considered cultivation, harvesting and conversion options. Coproduction of bio-energy with high-value products are more viable, but is not considered in this research.
Table 4 Economic indices of different microalgae cultivation methods. | ||
Items | Open space | Greenhouse |
Total value from production ($ kg–1) | 210341.00 | 275520.00 |
Total cost from production ($ kg–1) | 5964.41 | 6243.94 |
Net return ($ kg–1) | 204376.59 | 269276.06 |
Benefit-to-cost ratio (ratio) | 35.26 | 44.12 |
Productivity (kg $−1) | 0.17 | 0.16 |
3.4. DEA assessment
Table 5 presents that the average scores of TE, SE, and PTE are 0.853, 0.946, and 0.910 in the open space method whilst 0.912, 0.936, and 0.905 in the greenhouse method, respectively. The results show that SE has the highest standard deviation in the open space method. Lack of knowledge about the correct cultivation methods and lack of optimal allocation of resources are the main causes. It was also found that due to the large dispersion in TE, the surveyed farmers consumed too much different inputs. Khai and Yabe (2011) reported that TE was 0.816 for paddy production in Vietnam. Elhami et al. (2016) computed that TE, PTE, and SE for chickpea production in Isfahan province, Iran were 0.94, 0.99, and 0.94, respectively.
Table 5 Average efficiency items of different microalgae cultivation methods. | ||||||
Items | TE | PTE | SE | |||
Open space | Greenhouse | Open space | Greenhouse | Open space | Greenhouse | |
Average | 0.853 | 0.912 | 0.946 | 0.936 | 0.910 | 0.905 |
Standard deviation | 0.070 | 0.060 | 0.090 | 0.040 | 0.080 | 0.030 |
Minimum | 0.683 | 0.652 | 0.693 | 0.647 | 0.651 | 0.694 |
Maximum | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
The CCR and BCC models are depicted in Fig. 3. According to the results of the CCR model, with TE and PTE computed, TE scores in the open space and greenhouse methods are computed as to be 1 for 7 and 11 units, respectively. The results of the BCC model show the PTE scores of an effective open space and greenhouse cultivation of 15 and 13 units, respectively. SE scores in open space and greenhouse cultivation of 1 are obtained for 9 and 7 units, respectively.
|
|
Fig. 3. The efficiency score distribution in different microalgae cultivation methods. |
3.3. Energy saving
Table 6 shows the optimum energy use and saving of microalgae cultivation systems (open space and greenhouse). Based on the DEA results, the total optimized consumption of energy is 14476.93 MJ kg-1 in the open space method. On another hand, a saving of 16355.21 MJ kg-1 (i.e., a 7.55% decrease) versus the present cultivation conditions is attained in the greenhouse method by changing inefficient into efficient cultivation. The highest amount of energy saving in water and electricity was computed at 26.67% and 18.17% in the greenhouse method, respectively. In the open space cultivation method, the greatest saving (16.89%) was achieved in electricity. The DEA results also showed that the total optimized energy consumption was 14476.93 MJ kg-1 in open-space cultivation. In the open space method, changing inefficient cultivation into efficient cultivation resulted in an energy saving of 1443.47 MJ kg-1 (9.06%) versus the present method. Also, in the greenhouse method, the optimized energy consumption was 16355.21 MJ kg-1, showing a 7.55 percent decrease versus the present method.
Table 6 Optimum energy requirement and saving energy in different microalgae cultivation methods. | ||||||
Inputs | Optimum energy requirement (MJ ha-1) | Saving energy (MJ ha-1) | Saving energy (%) | |||
Open space | Greenhouse | Open space | Greenhouse | Open space | Greenhouse | |
1. Human labor | 459.22 | 364.58 | 11.17 | 47.01 | 2.37 | 11.42 |
2. Machinery and equipment | 964.92 | 1050.27 | 115.07 | 119.72 | 10.65 | 10.23 |
3. Electricity | 249.32 | 294.55 | 50.67 | 65.44 | 16.89 | 18.17 |
4. Water | 1007.48 | 659.92 | 192.51 | 240.07 | 16.04 | 26.67 |
5. Natural gas | 11795.96 | 13985.87 | 1074.03 | 864.12 | 8.34 | 5.81 |
Total energy input | 14476.93 | 16355.21 | 1443.47 | 1336.38 | 9.06 | 7.55 |
4. Conclusions
This study investigated energy use and economic analysis of different methods of microalgae cultivation in which DEA was used to optimize energy consumption. According to the results of energy indices for the open space and greenhouse methods, the energy ratio was 0.89 and 0.80, the energy productivity was 0.06 and 0.02 kg MJ−1, and the specific energy was 15.92 and 17.69 MJ kg-1, respectively. Natural gas was found to be a significant input in different cultivation methods based on its contribution to energy use. The results of the economic analysis showed that the benefit-cost ratio in the greenhouse method was significant. The productivity of the open space and greenhouse methods was reported at 0.17 and 0.16 kg $−1, respectively. The optimization of energy consumption in the open space method was 14476.93 MJ kg-1 and the energy saving was 1443.47 MJ kg-1, showing about a 9.06% decrease compared to the present method in open space cultivation. Also, in the greenhouse method, the optimization of energy consumption amounted to 16355.21 MJ kg-1 (i.e., a 7.55% decrease versus the present method). According to the results of energy consumption and economic analysis, it was found that the open space method of production is economical in terms of energy and economy. It is also suggested to investigate the modeling process for algae production in different cultivation systems to determine the amount of energy consumption for the coming years.
Declarations
Ethics approval and consent to participate: Not applicable.
Conflict of interest: The authors declare that they have no competing interests.
Funding: Not applicable.
Authors Contributions: Naser Kazemia: Data curation, Validation, Writing-Original draft, Reviewing and Editing, Mohammad Gholami Parashkoohi: Methodology, Reviewing and Editing, Supervision, Ahmad Mohammadi: Validation, Writing- Reviewing and Editing, Supervision, Davood Mohammad Zamani: Investigation, Writing-Reviewing and Editing, Formal analysis, Software.
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