
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12903-9
10.1016/j.heliyon.2024.e36872
e36872
Research Article
Simulation of biocrude production from P. tricornutum, S. platensis, and C. vulgaris using Aspenplus®
Tushar Mohammad Shahed H.K. mstushar@me.ruet.ac.bd
⁎
Islam Md Shafikul saem.shafik10@gmail.com

Ahmmed Taufique taufiqueahmmedraj@gmail.com

Joarder Md Sadman Anjum sadman.anjum142114@gmail.com

Department of Mechanical Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi, 6204, Bangladesh
⁎ Corresponding author. mstushar@me.ruet.ac.bd
24 8 2024
15 9 2024
24 8 2024
10 17 e3687227 11 2023
1 8 2024
23 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Hydrothermal liquefaction (HTL) of biomass is performed at elevated pressure and temperature to avoid the drying process. This process is also suitable for the low grade biomass with higher moisture content. In this article, simulation of three types of microalgae species, such as Phaeodactylum tricornutum, Spirulina platensis, and Chlorella vulgaris, are performed using Aspen Plus®. Simulation conditions, for instance, temperature, proximate and ultimate analyses, feed rate, water content, component names, etc., are taken from the literatures. The results of microalgae are then compared at two different temperature conditions. The values, however, are not the same for all the materials due to the data availability from the literature. The highest calorific value is obtained from C. vulgaris; it is 37.27 MJ/kg at 621K, and the highest energy recovery and energy ratio are obtained from P. tricornutum; they are 88.78 % and 1.86, both at 648K respectively. The difference between experimental and simulated calorific values of different biocrudes are ranging from 2.7 % to 3.62 % at higher temperatures and from 4.68 % to 10.72 % at lower temperatures. Finally, it is found that the simulation results corroborate with the experimental results with minimal errors.

Graphical abstract

Image 1

Keywords

Micro algae
Simulation
Hydrothermal liquefaction
Aspen plus
Renewable energy
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pmc1 Introduction

In recent years, energy demand has taken a sharp hike due to rapid urbanization and industrialization around the world. To meet this energy demand, conventional energy sources like coal, oil, and natural gas have been used extensively [1]. This excessive use of fossil fuels and rapid urbanization are the causes of increasing anthropogenic CO2 emission in the atmosphere at an alarming rate, which results in various adverse environmental effects like climate change due to global warming [2,3]. As such, renewable energy sources like biomass, solar, wind, and hydropower have been extensively researched [4]. Among them, biomass is a source of clean, renewable energy that is increasingly being used to make sustainable biofuels all around the world [5,6]. As the name implies, HTL is the strategy to treat biomass with higher moisture content to produce biocrude, thus the process is energy and cost effective [7]. This biocrude can be further processed to get various chemicals, gasoline, diesel or may also be used as feed for biohydrogen production through supercritical gasification process [8]. Water is typically used as the solvent in pressure and temperature reactions [[9], [10], [11]]. The solid byproduct is termed as hydro-char (bio-char) and may also be used as fuel for various processes [12].

The HTL process is also advantageous as it does not deliver any harmful products (i.e., NOx, SOx, or NH3), rather nitrogen, sulfur, and chlorine are converted into harmless by-products such as N2 and inorganic acids that can be neutralized with bases. The water used in the HTL process acts as both a solvent and a reactant. At elevated pressure and temperature, water start to behave as a nonpolar liquid and dissolve the biomass easily to produce the biocrude and other byproducts. The nonpolar nature of water aids in the solvation of biomass compounds, whereas H+ and OH- ions aid in the conversion of biomass molecules into more desirable compounds. The HTL process has been shown to produce bio-oils with energy densities between 35 and 37 MJ/kg, which is nearer the energy density of diesel oil (42 MJ/kg) [13,14]. Aspen Plus is one of the main chemical process software packages that permits to fabricate a process model and simulate it utilizing complex computations (equations, models, regressions, math calculations, and so on). There are numerous uses for Aspen Plus, from planning new processes to creating existing ones. Aspen Plus ensures simulation measures with efficient work processes [15]. Moreover, it is used in chemical process planning and improvement as commercial simulation and modeling software, which has different applications in different industries [16,17]. Microalgae is drawing the attention of the researchers due to its potential to produce renewable fuels. However, the HTL process is the most suitable biocrude production from microalgae as it has higher moisture content [16].

The HTL process is mostly influenced by temperature, reaction time and biomass loading [18]. With the increase in temperature, the yield of gas and bio-oil also increases, and the yield of water-dissolved organic compounds and solid products decreases. Additionally, the oxygen content decreases and nitrogen content increases in the product when the process temperature increases. The increase in nitrogen content is connected to a more thorough conversion of the MA's protein component. A shorter time is required for MA conversion at higher temperature [12]. The HTL of microalgae is advantageous as the process is not high-energy intensive because of the low operating temperature and also there is no need for pretreating and drying the feed. All the components of algae, like protein, lipid, and carbohydrates can be converted into bio-oil with enhanced HHV products, and the energy-to-weight ratio is high compared to the initial components [19].

HTL is alluded to literatures as a promising process for the continuous development of biofuels. Nevertheless, accurate results may not be achieved from the simulation because of the unavailability of the exact reaction schemes that occur during the hydrothermal process. The selection of different types of product components is unique for different biomass types to improve the accuracy of the process analysis. In the simulation model, identical components are chosen for the biocrude, resulting in a small variation in calorific value for all types of algae. Since the process analysis is not kinetic reaction based, varying the temperature or pressure is not feasible in terms of yield optimization.

Microalgae, also known as phytoplankton, are algae that exist in groups, individually, or in chains. The size of microalgae can range from 1 μm to a couple of millimeters. These live in both freshwater and seawater; a great many plant varieties have been accounted for; however, numbers vary in the literature [16]. The amount of lipids, proteins, and carbohydrates in microalgae changes depending on the species and growing circumstances [20]. Photosynthesis is the process by which light energy is converted into chemical energy that plants and organisms can use. These microalgae use light as energy to convert CO2 into carbohydrates and deliver oxygen. The microalgae are comprised of the components O, N, C, and H, as well as proteins, carbohydrates, and lipids [16].

Due to the popularity of the HTL process, there have been an enormous number of experiments and simulations of different biomass materials in recent times. Many experts chose microalgae because of its availability, high moisture content, variety of species, and other factors. Aspen Plus was chosen for the process analysis as one of the most convenient and well-established simulation programs. Ziba Borazjani [21] et al. show a global kinetic model for HTL using algae, a way to optimize parameters, and an economic analysis of the Aspen Plus product yields. The model has excellent predictive ability, with Scenedesmus sp. being more suitable for grease residue yield prediction. Shia et al. [22] present a rigorous HTL model for microalgae, focusing on characterization, reaction pathways, kinetics, and thermodynamics. It predicts biocrude yields and HHV under various conditions and recommends multi-objective optimization for process design and economic analysis. Ranganathan [23] looks at how HTL can be used to make renewable fuels from sewage sludge. He focuses on wastewater treatment, making biocrude, hydro-processing, and making hydrogen. It demonstrates feasibility and guides future plant design towards the circular economy. Borazjani et al. examine biocrude production using HTL of algae. The heat exchanger has the highest energy loss and lowest efficiency values, while the HTL reactor has high efficiency (99.9 %) due to complex reactions. Naaz et al. [24] conducted life cycle assessments on three algal biofilm-based conversion systems: wastewater-grown algae (WWA), HTL, and synthetic media-grown algae (SMA). Results showed HTL had a 41.1 % lower environmental impact, minimal energy input, and reduced human health impacts.

According to the aforementioned current literature, investigations with experimental outcomes for HTL's Aspen Plus have been conducted. As a result, it can be assumed that the process warrants analysis regarding production and viability. The works are primarily focused on energy balance, consumption rate, economic analysis, environmental impact, and health factors, as can be observed from the literature mentioned above. In these experiments, there is no article available on process comparison and error analysis. In this article, the experimental data of three specific species of microalgae; namely slurry of P. tricornutum, S. platensis, and C. vulgaris are simulated and compared, and analyzed the errors in order to validate the procedure. Using Aspen Plus, a continuous algae HTL process simulation was planned, designed, and simulated. The main focus of this study is on biocrude energy and biomass energy from algae, respectively, which were compared in this research paper. The results were obtained by simulation, considering data such as mass balance, energy consumption, higher calorific value, energy recovery, etc. from the literature. We tried to establish the possibility of characterizing algae for the production of biocrude by HTL and compare the outcome of the analysis at different temperatures. We compared the higher heating value of the simulation with the results found in the literature at different temperatures for those three different species of microalgae.

In this article, three different species of algae, P. tricornutum, S. platensis, and C. vulgaris, are chosen for their significance to the production of biocrude. Using Aspen Plus®, a continuous algae HTL process is simulated focusing on biocrude derived from algal biomass. The main objective of the study is to obtain results by simulation, such as mass balance, energy consumption, higher calorific value, energy recovery, etc. Therefore, it can be possible to characterize algae for the production of biocrude by HTL and compare the outcome of the analysis at different temperatures. Besides, another objective of this study is to compare the higher heating value of simulation with the results found in the literature.

2 Novelty of the research

This manuscript introduces a novel simulation study using Aspen Plus software to analyze the hydrothermal liquefaction (HTL) of microalgae, focusing on three types—Phaeodactylum tricornutum, Spirulina platensis, and Chlorella vulgaris. By integrating experimental and simulated data, the study examines biocrude yield, energy recovery, and higher heating values (HHV), areas not extensively covered in previous literature. It is distinguished by its detailed error analysis and the accuracy of its simulation predictions for HTL outcomes. The following points are noteworthy in this study.• This study conducts HTL simulations to analyze temperature effects on the three microalgae species, comparing their process efficiency and output quality. The study integrates real-world experimental results with simulated data, with a focus on yield, energy recovery, and the higher heating value (HHV) of biocrude.

• It investigates how varying operational temperatures influence the quality and yield of specific biocrudes. This study provides valuable insights into HTL simulation and predicts the process's suitability for specific algae species by comparing their results.

• It utilizes continuous process simulation to allow precise adjustments based on real data, enhancing the system's applicability and reliability. Further, it assesses the scalability of converting algae into biocrude, thereby contributing valuable data to renewable energy resources, and promoting the development of other sustainable energy solutions.

3 Physical properties of biocrude

Biocrude properties can be rigorously assessed and compared with those of traditional diesel or biodiesel, areas that have sparked significant interest in research. Analyzing the physical properties of biocrude provides insights into their performance and usability as fuel alternatives. One key property, viscosity, indicates the fluidity of the fuel, which is crucial for numerous flow measurements in liquid systems. The molecular structure of the hydrocarbons influences their viscosity; typically, hydrocarbons with straight chains exhibit higher viscosity compared to branched chains. In comparison, compounds containing acid or alcohol groups have a more pronounced impact on increasing viscosity than ketones and esters. For fuel applications, kinematic viscosity is especially relevant. Biofuels derived from biocrude with high viscosity do not atomize well, leading to inefficient combustion, increased engine residue, and higher energy needs for fuel pumping. Moreover, high-viscosity fuels tend to raise carbon monoxide (CO) emissions. Conversely, fuels with low viscosity may cause inadequate lubrication in the injection system, potentially causing leaks and increased engine wear. Hence, to balance these effects, biodiesel standards include specified upper and lower limits for kinematic viscosity [25,26].

Density is another critical biocrude property, directly correlating with energy content. Engines that measure fuel intake by volume benefit from denser fuels, which yield more energy per unit mass upon combustion. Higher fuel density is also associated with increased emissions of CO, nitrogen oxides (NOx), and unburned hydrocarbons (UHC). The specific gravity of a fuel, which is sometimes reported instead of density, provides an indirect measure of these properties [27,28].

The heating value of biocrude determines its energy content and is a fundamental metric for evaluating the efficiency of conversion processes such as HTL. Either a higher heating value (HHV) or a lower heating value (LHV) expresses the heat of water vaporization during combustion, while the latter does not. Research reveals a close relationship between the carbon and hydrogen contents of HTL-made biocrudes and their heating value. The heating value increases as carbon and hydrogen levels rise while decreasing as oxygen and nitrogen levels rise [29,30].

The water content of biocrude is a crucial factor that significantly impacts its combustion efficiency and energy value. The water content in biocrude can range widely from 10 % to 30 %, depending on the feedstock and the conversion process used. High water content can lead to lower energy output and reduced combustion efficiency, making it a critical parameter to manage for effective utilization of biocrude [31,32].

Oxygen content is another important property, with biocrude containing up to 40 % oxygen. This high oxygen content affects the stability and corrosiveness of the biocrude, leading to a lower energy density. As a result, biocrude often requires upgrading to reduce oxygen levels, thereby improving its fuel quality, and making it more suitable for various applications [28,33]. The acidity of biocrude is characterized by a low pH value, typically ranging from 2 to 4, due to the presence of organic acids. This high acidity can cause significant corrosion issues in storage and transportation equipment, posing challenges for long-term handling and infrastructure maintenance [34,35]. The solubility characteristics of biocrude are essential for understanding its interactions with other solvents and its processing behavior. Biocrude is generally immiscible with water but can exhibit partial solubility in organic solvents, which can influence its blending and refining processes [27].

Elemental composition is a fundamental aspect of biocrude, influencing its chemical behavior and potential uses. Typically, biocrude consists of 50–60 % carbon, 5–8% hydrogen, 30–40 % oxygen, up to 5 % nitrogen, and less than 1 % sulfur. This composition highlights the need for upgrading processes to enhance its suitability as a fuel and reduce undesirable elements like oxygen and sulfur [36,37]. The boiling range of biocrude is broad due to its complex mixture of compounds. This property is important as it provides insights into the volatility and fractionation potential of biocrude, informing refining strategies to separate it into useful fractions for different applications [31].

The ash content of biocrude, representing the non-combustible residue left after burning, affects its handling and processing. Although typically low, the ash content can vary based on the feedstock, and managing this residue is crucial for optimizing the performance and maintenance of combustion systems [38]. However, there are other biocrude properties that include flash point, cloud point, pour point, oxidation stability, etc. The flash point indicates the temperature at which a fuel can vaporize, making it safer during storage and handling. The cloud point is the temperature below which wax forms visible crystals, especially in colder climates. The pour point represents the lowest temperature at which a fuel maintains its fluidity and is suitable for pumping. Oxidation stability measures how resistant a fuel is to oxidation, which can lead to harmful acids, gums, and sediments. Winterization allows for the engineering of biofuels, particularly biodiesel, to have lower pour points [26,39,40].

4 Methodology

4.1 Simulation model

In this article, the Aspen Plus® is used to analyze a HTL process through designing and modeling, and to analyze and forecast different operations of the process. The main objective of this study is to build a model for the HTL process to convert algae into four output products: biocrude, solid, aqueous, and gas.

4.2 Description of process

Two types of models HTL plants are used in this article. First plant is for simulating the HTL process (Fig. 3) and second plant is for measuring the calorific value of the biocrude (Fig. 4). The HTL process simulation consists of a pump, reactor (R-Yield), a separator and heater. Various streams are used to connect all the input and output. Feeds enter the system through the input stream and yields are separated into the separator as the main four types of products as biocrude, solid, aqueous and gas. The second plant comprises of a mixer, reactor (R-Stoic reactor) and separator. Here, mixer is used to mix the air and biocrude, R-stoic reactor is used for the combustion of the biocrude and the separator is used to segregate the water and dry flue gas. There are several assumptions are taken during the simulation process. First and foremost, the operation is considered to be in a steady state. Next, the heat distribution and mixing of the input stream are uniform. In addition, pressure drop during the process is neglected. Finally, the biomass particle sizes are assumed to be uniform.

To assign the structural components of the biomass, proximate analysis is utilized. Four structural elements are moisture content (MC), volatile substance (VS), fixed carbon (FC), and ash [41]. The ultimate analysis, on the other hand, is used to measure the chemical elements (C, N, O, H, and S) of biomass. Ash content is estimated by proximate analysis and O content is determined by difference from ultimate analysis [42,43]. The proximate and ultimate analyses data of different microalgae species uses in this study are shown in Table 1.Table 1 Ultimate and proximate analysis of microalgae [16].

Table 1Microalgae Species	C%	N%	H%	O%	S%	MC%	Ash%	FC%	VS%	
Chlorella	53.5	11.0	7.4	27.6	0.5	5.2	6.0	–	–	
C. vulgaris1	52.6	8.2	7.1	32.2	0.5	5.9	7.0	–	–	
C. vulgaris2	40.8	6.7	6.5	43.1	1	4.4	15.9	–	–	
Spirulina sp.	39.26	6.65	6.11	47.41	0.57	8.45	13.99	12.08	65.48	
S. platensis	46.87	10.75	6.98	34.86	0.54	–	6.60	15.25	78.15	
Spirulina1	55.7	11.2	6.8	26.4	0.8	7.8	7.6	–	–	
Spirulina2	53.7	12.1	7.7	25.9	0.6	5.7	7.6	–	–	
P. tricornutum1	38.0	5.2	4.8	51.3	0.7	–	5.2	–	–	
P. tricornutum2	57.03	8.0	7.46	24.97	1.28	–	12.45	–	–	

4.2.1 HTL plant

Fig. 1 shows the design of the simulated HTL plant. In this plant, water and biomass slurry are fed and pressurized through the pump before entering the reactor. The mixture of feed and water is then preheated to the desired temperature by using the pre-heater. The hot slurry is then taken through the reactor to obtain the four products. The biocrude, gas, solid, and aqueous products are distributed into their streams by the separator.Fig. 1 Plant 1 for the HTL process flowsheet.

Fig. 1

4.2.2 Biocrude upgradation plant

Fig. 2 shows the design of the second plant used for upgrading the biocrude. The air-biocrude mixture is combusted in the R-Stoic reactor. The combustion products like flue gas and water are separated by the separator after combustion.Fig. 2 Plant 2 for HTL biocrude upgradation.

Fig. 2

Fig. 3 Change of HHV with respect to temperature for three different algae species. (a) P. tricornutum, (b) C. vulgaris, (c) S. platensis.

Fig. 3

Fig. 4 Percentage of energy recovery at different temperatures. (a) Energy recovery at higher temperature (b) Energy recovery at lower temperature.

Fig. 4

During simulation, conventional and non-conventional components are added in the first plant. Water and the products other than the solid phase are conventional, and the biomass and the solid phase of the products are non-conventional. The density and enthalpy of the non-conventional components are measured using the DCHARIGT and HCOALGEN models. Non-conventional components are defined by their sulfur, ultimate, and proximate analyses. The values of those three analyses are entered in PROXANAL, ULTANAL, and SULFANAL. Sixteen biocrude components, twelve aqueous components, five gaseous components, and two solid phase components are used in the simulation yield portion. Major parts of the aqueous phase are water, and another relevant component is soluble in the water. Major components in the gaseous phase are CO2 and hydrogen. The number of components in products is taken from literature [44]. Different algae have different numbers of components, which are calculated and taken as input into the simulation. Ash and char are used as the solid phase in the simulation. Three types of algae are used with different temperatures, pressures, and yields. The operational conditions used in the process analysis are listed in Table 2.Table 2 Operational conditions for simulation.

Table 2Algae Species	Pressure (bar)	Temperature (K)	Biocrude Yield (wt.%)	
P. tricornutum	270	648	54	
270	523	41	
C. vulgaris	220	621	38	
220	523	33	
S. platensis	120	573	31	
120	513	27	

In the upgradation plant, biocrude consisting of sixteen components are given in the biocrude stream, and air containing 79 % nitrogen and 21 % oxygen is given in the air stream. The inlet and exit temperatures of the reactor are considered 25 °C for HHV and LHV, and the outlet temperature is considered to be 150 °C with the same inlet temperature. A separator is used to segregate the water and the dry flue stream. The dry flue gas contains the gaseous products produced during combustion. The heating value is calculated using the heat duty and total mass flow rate of biocrude. The chemical reaction at the time of combustion is required in an R-stoic reactor. Each biocrude component participates in the combustion reaction. Hydrogen in the biocrude component is converted to water, carbon is converted to carbon-di-oxide, nitrogen is converted to nitrogen gas, and sulfur is converted to sulfur-di-oxide or sulfur-tri-oxide gas.

4.3 Heat and energy Evaluation

The efficiency of energy conversion is calculated on the basis of a lower heating value by the equation (1) [16]. This equation consists of the mass and lower heating value of biocrude and algae, the heat of the heater, and the work done of the pump.(1) η=LHV(Bio−crude)×m(Bio−crude)LHV(Algae)×m(Algae)+W(Pump)+Q(Heater)×100%

The main purpose of HTL is to convert the biomass to a product with higher HHV than the biomass itself. The percentage of energy recovery is calculated from the equation (2) [16]. This equation shows the recovery of energy from biocrude with the ratio of the heating values of biocrude and biomass.(2) %ofEnergyrecovery=HHV(Bio−crude)×m(Bio−crude)HHV(Algae)×m(Algae)×100%

4.4 Aspenplus® simulation

The conditions and components used in simulation are shown in Table 3, Table 4. Three algae species are used in the software, and for simplification, acronyms are used as PH for Phaeodactylum tricornutum, CH for Chlorella vulgaris, and SP for Spirulina platensis.Table 3 Proximate and ultimate analyses (%, mass basis) [16].

Table 3		PH	CH	SP	
Proximate:	Moisture Content	7.5	5.5	5.7	
Volatile Matter (dba)	77.9	82.4	79.6	
Ash (db)	15.8	5.4	8	
Fixed Carbon (db, by difference)	6.3	12.2	12.4	
Ultimate:	Carbon	44.2	51	48.5	
Hydrogen	6.9	7	6.8	
Nitrogen	7.4	9.5	11.2	
Ash	13.4	6.6	7	
Oxygen	28.1	25.9	26.5	
a db = dry basis.

Table 4 Conditions and specifications used in Aspen Plus [18,[44], [45], [46], [47]].

Table 4	PH [18,45]	PH [45]	CH [44]	CH [44]	SP [46]	SP [47]	
Initial pressure (bar)	1	1	1	1	1	1	
Initial temperature (K)	288	288	288	288	288	288	
Slurry feed rate (kg/h)	300	300	300	300	300	300	
Operational conditions	
Pressure (bar)	270	270	220	220	120	120	
Temperature (K)	523	648	523	621	513	573	
Solid Content (wt. %)	7	7	19	19	21	21	
Water content (wt. %)	93	93	81	81	79	79	
Yield	Wt. %	Wt. %	Wt. %	Wt. %	Wt. %	Wt. %	
Biocrude	41	54	33	38	27	31	
Aqueous	32	12	63	59	54	23	
Gas	18	27	0.5	1	7	35	
Solid	9	7	3.5	2	12	11	

Algae is used in the software as a non-conventional component, and the proximate and ultimate analyses are used for defining it. The values for the ultimate and proximate analyses are taken from the literature [16]. In literature, the approximate value of moisture percentage is derived from thermogravimetric analysis. The proximate and ultimate analyses of algae are shown in Table 3.

Table 4 illustrates the initial conditions, which include pressure, temperature, and slurry feed rate, as well as the subsequent operational conditions, which include pressure, temperature, solid content, water content, and finally the yield percentage for the biocrude, aqueous, gases, and solids products utilized in Aspen Plus.

Different types of components, obtained from literature, are taken for the simulation process using Aspen Plus. These components are shown in the tables in the annex section. Biocrude contains hydrocarbons, ester, oxygenates, organic acids, nitrogenates, phenols, etc., and the aqueous phase contains cyclopentenones, phenolics, carboxylic acids, nitrogen heterocycles, etc. The solid phase in HTL contains ash and char as yields that are not available in the library of Aspen Plus software and hence, they are considered as non-conventional components. Because there is no reference for char and ash compositions, both char and ash are assumed to be 100 percent ash in ULTANAL and PROXANAL. For components of gases, CO2, CO, H, N, and CH4 are used in the Aspen Plus software.

5 Results and discussion

5.1 Mass balance

Table 5 details the mass balance of three species of algae—P. tricornutum, C. vulgaris, and S. platensis—processed through HTL at different temperatures. Utilizing 21 kg/h of P. tricornutum results in 8.6 kg/h of biocrude at 523 K and 11.4 kg/h at 648 K. For C. vulgaris, at an input of 56 kg/h, the biocrude output is 18.5 kg/h at 523 K and increases to 21.5 kg/h at 621 K. Similarly, S. platensis processed at 60 kg/h yields 16.2 kg/h of biocrude at 513 K and 18.7 kg/h at 573 K. However, between aqeuos, water, solids, and gas, the majority of the HTL output across all species and conditions is water, followed by aqueous, with a smaller fraction in the solids and gas phases. This mass balance demonstrates the efficiency of resource utilization in HTL processes, indicating that temperature significantly affects biocrude yield while maintaining consistent total mass throughput.Table 5 Mass balance of three species of Algae in Simulation.

Table 5		P. tricornutum	C. vulgaris	S. platensis	
Input (kg/h)	Temp.	288 K	288 K	288 K	
Algae	21	21	56	56	60	60	
Water	279	279	244	244	240	240	
Sum	300	300	300	300	300	300	
Output (kg/h)	Temp.	523 K	648 K	523 K	621 K	513 K	573 K	
Biocrude	8.6	11.4	18.5	21.5	16.2	18.7	
Aqueous	6.8	2.5	35.3	32.9	32.4	13.9	
Water	279	279	244	244	240	239.7	
Solid	1	1.6	2	1.1	7.2	6.5	
Gas	3.7	5.5	0.2	0.5	4.2	21.2	
Sum	300	300	300	300	300	300	

5.2 Energy consumption

Table 6 presents the energy consumption metrics for the HTL process of three algae species—P. tricornutum, C. vulgaris, and S. platensis—under different operational temperatures. For P. tricornutum, energy consumption is recorded at 107 kW at 523 K and rises significantly to 250 kW at 648 K, reflecting the increased energy requirements of the heater and pump as temperatures and pressures (270 bars) escalate. When processing C. vulgaris at 523 K and 621 K under 220 bars, the energy demand increases from 107 kW to 159.3 kW. S. platensis, processed at lower pressures of 120 bars and temperatures of 513 K and 573 K, shows a moderate increase in energy consumption from 103.9 kW to 117.1 kW. The data clearly demonstrates that higher temperatures and pressures necessitate increased power consumption, predominantly in heating and pumping, to maintain the required conditions for optimal liquefaction efficiency.Table 6 Consumption of energy.

Table 6	P. tricornutum	C. vulgaris	S. platensis	
Temperature	523 K	648 K	523 K	621 K	513 K	573 K	
Pump (kW)	2	7.0	2	4.5	1.9	2.5	
Heater (kW)	105	243	105	154.8	102	114.6	
Total (kW)	107	250	107	159.3	103.9	117.1	

5.3 Higher heating value (HHV) at different temperature

Table 7 presents the simulated higher heating values (HHV) for three algal species—P. tricornutum, C. vulgaris, and S. platensis—at two distinct temperature settings. For C. vulgaris, the HHV is observed at 32.79 MJ/kg at 523 K and increases to 37.27 MJ/kg at 648 K. S. platensis shows a slight variation in HHV, recording 32.84 MJ/kg at 513 K and 33.12 MJ/kg at 573 K. Similarly, P. tricornutum demonstrates an increase in HHV from 33.49 MJ/kg at 523 K to 34.93 MJ/kg at 648 K. These results underscore the significant impact of temperature on the heating values of biocrude derived from these specific algal species, indicating enhanced energy yield at higher temperatures due to reduced moisture and volatile content, thus improving the combustibility of the biocrude.Table 7 HHV from biocrude at two different temperatures.

Table 7	P. tricornutum	C. vulgaris	S. platensis	
Temperature	523 K	648 K	523 K	621K	513K	573K	
HHV from Simulation (MJ/Kg)	33.49	34.93	32.79	37.27	32.84	33.12	

The HHV of the HTL process increases with the temperature. Fig. 3 shows how the heating value of three substances—P. tricornutum, C. vulgaris, and S. platensis—increases with temperature. Essentially, an increase in temperature leads to the thermal removal of more bound moisture and volatile matter. This makes the substances richer in fixed carbon, thus making the species burn more effectively. As a result, their heating value, which is a measure of how much energy they release when burned, goes up.

5.4 Energy recovery at different temperature

Fig. 4, derived from simulation data, quantitatively illustrates the energy recovery percentages for three algae species—P. tricornutum, C. vulgaris, and S. platensis—under two temperature conditions. P. tricornutum gains the highest energy recovery, achieving 88.78 % at the temperature of 648K and 85.12 % at 523K. C. vulgaris demonstrates a moderate energy recovery, achieving 59.8 % at 621K and 52.61 % at 523K. S. platensis records the lowest energy recovery percentages, with 44.97 % at 573K and 44.59 % at 513K.

As per Fig. 4 and Table 8, the percentage of energy recovery is greater at high temperatures and smaller at low temperatures. As the temperature rises beyond the bound moisture limit, the volatile matter is removed, leaving mostly fixed carbon, increasing the combustibility of the material and, as a result, the percentage of energy recovery.Table 8 Percentage of energy recovery at different temperature.

Table 8	P. tricornutum	C. vulgaris	S. platensis	
Temperature	523K	648K	523K	621K	513K	573K	
Energy recovery (%)	85.12	88.78	52.61	59.8	44.59	44.97	

5.5 Energy from biocrude and microalgae

Fig. 5 shows the HHV of algal biomass and biocrude obtained from the HTL process, demonstrating that biocrude's HHV is significantly higher than that of biomass. This indicates that biocrude yields more energy compared to biomass. During the HTL process, several reactions occur, including hydrolysis, depolymerization, condensation, re-polymerization, and thermal cracking. These reactions effectively upgrade the biomass into a comparatively better and improved biofuel.Fig. 5 Comparison of HHV between raw and biocrude. (a) HHV at higher temperature. (b) HHV at lower temperature.[18, 44–47].

Fig. 5

Energy is increased due to the conversion of biomass into biocrude in the HTL plant. Based on the LHV, Ebiocrude/Ealgae is measured in the product and feed streams. Ebiocrude and Ealgae is the amount of energy that contains by the biocrude and algae respectively. High ratios are obtained from P. tricornutum, and low ratios are obtained from S. platensis. The values are 1.86 at 648K and 1.77 at 523K for P. tricornutum, 1.64 at 621K and 1.53 at 523K for C. vulgaris, and 1.53 at 573K and 1.46 at 513K for S. platensis. Fig. 6 depicts the energy ratio of biocrude and algae. A higher energy ratio is obtained for P. tricornutum because of its low LHV for biomass.Fig. 6 Energy ratios of biocrude from algae species. (a) Energy ratios at higher temperature. (b) Energy ratios at lower temperature [18,[44], [45], [46], [47]].

Fig. 6

5.6 Higher heating value (HHV) from simulation and literature

The HHVs of different biocrude species are obtained by simulation. The HHV of 37.27 MJ/kg at 648K and of 32.79 MJ/kg at 523K are obtained for C. vulgaris. The HHV of 33.12 MJ/kg at 573K and of 32.84 MJ/kg at 513K are obtained for S. platensis. The HHV of 33.49 MJ/kg at 523K and of 34.93 MJ/kg at 648K are obtained for P. tricornutum. The HHV at different temperatures from the biocrude of three algae species is illustrated in Table 9. Here, 4.68 % and 3.62 % energy difference are obtained for C. vulgaris, 9.1 % and 7.74 % difference for S. platensis, and 10.52 % and 2.7 % difference are obtained for P. tricornutum. This difference might be due to not using all the components of biocrude, as the components reported in the articles are used for the calculation. The HHVs obtained from literature and simulation at different temperatures from the biocrude of the three algae species are illustrated in Fig. 7.Table 9 The HHV of biocrudes at two different temperatures [18,[44], [45], [46], [47]].

Table 9	P. tricornutum	C. vulgaris	S. platensis	
Temperature	523 K	648 K	523 K	621K	513K	573K	
HHV from Simulation (MJ/Kg)	33.49	34.93	32.79	37.27	32.84	33.12	
HHV from Literature (MJ/Kg)	30.3	35.9	34.4	38.67	30.1	35.8	
Error with experimental HHV	10.52 %	2.7 %	4.68 %	3.62 %	9.1 %	3.62 %	

Fig. 7 HHV of biocrude from literature and simulation at different temperatures. (a) HHV at higher temperature. (b) HHV at lower temperature [18,[44], [45], [46], [47]].

Fig. 7

5.7 Analysis of the variation obtained from simulation

In simulation, three types of algae — C. vulgaris, P. tricornutum, and S. platensis—are used. From the literature, C. vulgaris has the best fuel utilization properties, and it has the highest carbon and hydrogen percentages. The ash and nitrogen contents of C. vulgaris are also low. As such, the best HHV is obtained from this type of algae through simulation. P. tricornutum shows the most promising results. Higher energy recovery percentages and a higher energy ratio are obtained from P. tricornutum for both higher and lower temperatures. C. vulgaris is the most appropriate for fuel utilization as it shows a larger HHV than other species. A small amount of variation in the energy of the biocrude is observed in the simulation due to the unavailability of all components of biocrude in literature and limited petrochemical knowledge, the selection of an accurate amount of each component can be affected. Besides, we considered uniform mixing, constant pressure, perfect transformations, and uniform biomass particle size have been considered. In reality, it is very hard to maintain the parameters perfectly, uniformly, or continuously. As such, the simulation results differ from the experiment.

6 Conclusions

In this article, the HTL process is simulated through Aspenplus® to get an insight into the process analysis of biofuel production algae. The data for the simulation of three different algae, C. vulgaris, P. tricornutum, and S. platensis, is obtained from the literature. A total mass of 300 kg/h is maintained throughout the simulation process for all the algae species. The energy recovery is higher in case of P. tricornutum (88.78 % at 648K) compared to other algae species. Energy ratios are always greater than one and has increased with temperature. The highest energy recovery is found to be for P. tricornutum (1.86 at 648K). The higher heating values of biocrude are always greater than those of raw materials and increased with the increase in temperature. For a more suitable component selection, further study is required on the behavior of the biomass and final products. In addition, a kinetic reaction simulation model can be designed to carry out yield optimization and upscaling the system.Table A.1 Aqueous components

Table A.1Component ID	Type	Component Name	Alias	
Methanol	Conventional	Methanol	CH4O	
Ethanol	Conventional	Ethanol	C2H6O-2	
Acetone	Conventional	Acetone	C3H6O-1	
Formacid	Conventional	Formic-acid	CH2O2	
Aceti-01	Conventional	Acetic-acid	C2H4O2-1	
Glycerol	Conventional	Glycerol	C3H8O3	
CO2	Conventional	Carbon-dioxide	CO2	
NH3	Conventional	Ammonia	H3N	
3-pyrdol	Conventional	3-hydroxypyridine	C5H5NO	
1-eth-01	Conventional	1-ethyl-2-pyrrolidinone	C6H11NO-N8	
N-met-01	Conventional	N-methylthiopyrrolidone	C5H9NS	

Table A.2 Biocrude components

Table A.2Component ID	Type	Component name	Alias	
1E2pydin	Conventional	Epsilon-caprolactam	C6H11NO	
C5h9ns	Conventional	N-methyl-thiopyrrolidone	C5H9NS	
Ethylben	Conventional	Ethyl-benzene	C8H10-4	
P-cre-01	Conventional	P-cresol	C7H8O-5	
4Ephynol	Conventional	2-phenylethanol	C8H10O	
Indole	Conventional	Indole	C8H7N	
7Mindole	Conventional	Skatole	C9H9N	
C14amide	Conventional	Myristic-amide	C14H29NO	
C16amide	Conventional	Palmitic-acid-amide	C16H33NO	
Cis-9-01	Conventional	Cis-9-hexadecenoic-acid	C16H30O2-N4	
C16:0fa	Conventional	N-hexadecanoic-acid	C16H32O2	
C18facid	Conventional	Oleic-acid	C18H34O2	
Naphath	Conventional	Naphthalene	C10H8	
Cholesol	Conventional	Beta-cholesterol	C27H46O	
Aroamine	Conventional	N-n-diphenyl-p-phenylenediamine	C18H16N2	
Di-n--01	Conventional	Di-n-undecyl-phthalate	C30H50O4	

Table A.3 Gaseous components

Table A.3Component ID	Type	Component Name	Alias	
CO2	Conventional	Carbon-dioxide	CO2	
CH4	Conventional	Methane	CH4	
H2	Conventional	Hydrogen	H2	
N2	Conventional	Nitrogen	N2	
CO	Conventional	Carbon-monoxide	CO	

Table A.4 Solid components

Table A.4Component ID	Type	Component Name	Alias	
Ash	Nonconventional	–	–	
Char	Nonconventional	–	–	

CRediT authorship contribution statement

Mohammad Shahed H.K. Tushar: Writing – review & editing, Validation, Supervision, Investigation, Conceptualization. Md Shafikul Islam: Writing – original draft, Validation, Investigation, Formal analysis. Taufique Ahmmed: Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. Md Sadman Anjum Joarder: Writing – original draft, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The authors are grateful to Rajshahi University of Engineering & Technology (RUET) for providing necessary support and there is no conflict of interest in this research work.
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