Sustainability assessment of biomethanol production via hydrothermal gasification supported by artificial neural network

Global warming and climate change urge the deployment of close carbon-neutral technologies via the synthesis of low-carbon emission fuels and materials. An efficient intermediate product of such technologies is the biomethanol produced from biomass. Microalgae based technologies offer scalable solutions for the biofixation of CO2, where the produced biomass can be transformed into value-added fuel gas mixtures by applying thermochemical processes. In this study, the environmental and economic performances of biomethanol production are examined using artificial neural networks (ANNs) for the modelling of catalytic and noncatalytic hydrothermal gasification (HTG). Levenberg-Marquardt and Bayesian Regularisation algorithms are applied to describe the thermocatalytic transformation involving various types of feedstocks (biomass and wastes) in the training process. The relationship between the elemental composition of the feedstock, HTG reaction conditions (380 C–717 C, 22.5 MPa–34.4 MPa, 1–30 wt% biomass-to-water ratio, 0.3 min–60.0 min residence time, up to 5.5 wt% NaOH catalyst load) and fuel gas yield & composition are determined for Chlorella vulgaris strain. The ideal ANN topology is characterised by high training performance (MSE = 5.680E-01) and accuracies (R2 ≥ 0.965) using 2 hidden layers with 17-17 neurons. The process flowsheeting of biomass-to-methanol valorisation is performed using ASPEN Plus software involving the ANN-based HTG fuel gas profiles. Cradle-to-gate life cycle assessment (LCA) is carried out to evaluate the climate change potential of biomethanol production alternatives. It is obtained that high greenhouse gas (GHG) emission reduction (− 725 kg CO2,eq (t CH3OH) 1) can be achieved by enriching the HTG syngas composition with H2 using variable renewable electricity sources. The utilisation of hydrothermal gasification for the synthesis of biomethanol is found to be a favourable process alternative due to the (i) variable synthesis gas composition, (ii) heat integration, and (iii) GHG emission mitigation possibilities.


Introduction
The sustainable transformation and development of our society into a close zero-waste economy necessitate a whole-scale transition towards carbon emissions neutrality (Kerdlap et al., 2019). Drawing the directions of future-proof environmental technologies and the real advancement of chemical processes demand the minimisation of environmental footprints, as greenhouse gas (GHG) (Čuček et al., 2012), Nitrogen Footprint (Čuček et al., 2011 and also the other emissions footprints . The utilisation and deployment of variable renewable energy (VRE) sources (e.g., wind turbines and photovoltaic panels) have a determinant role in achieving ambitious climate goals and increasing the independence from conventional petrochemical materials (Deng and Lv, 2020). Harvesting the environmental benefits of clean fluctuating energy requires to (i) improve long-term and large-scale storage of intermittent renewable electricity (Liebensteiner and Wrienz, 2020), (ii) advance CO 2 capture and recycling technologies (Song et al., 2019), and (iii) design robust and feasible Power-to-X processes (Wu et al., 2021a).
Methanol, as a platform molecule, plays an important role in the framework of the circular economy (Fan et al., 2019), and it offers a wide variety of advantages (Olah, 2005). Methanol is a promising Power-to-Liquid (P2L) target compound that meets transportation, safety and infrastructure availability requirements (Zhang and Desideri, 2020). The storage of surplus variable renewable energy and decarbonisation of the chemical industry are unsolved challenges where sustainable methanol synthesis could have a high impact in the future. The conventional synthesis of methanol is based on the catalytic conversion of synthesis gas that is produced by the reforming of fossil resources (e.g., natural gas, coal) (Blumberg et al., 2019). Biomass-to-methanol (BTM) alternatives are gaining high interests due to the GHG emission reduction potentials over conventional technologies. Qin et al. (2021) discussed that applying coal-based methanol production with biomass co-gasification is a favourable technological pairing to decrease greenhouse gas emissions. It was also pointed out that the energy conversion efficiency of the BTM technology is lower compared to conventional alternatives. Hennig and Haase (2021) showed that a hydrogen enhanced BTM process could be operated at higher carbon and energy efficiencies compared to an unmodified BTM baseline scenario. However, it was highlighted that using alkaline electrolysis in the process makes biomethanol production highly unprofitable. These findings call attention to develop the technological readiness of bioenergy-based methanol production, including profitability (Butera et al., 2021), energy efficiency (Liu et al., 2021) and environmental aspects (Wu et al., 2021b). For these reasons, ex-ante synthesis and screening of promising Bioenergy with Carbon Capture and Utilisation (BECCU) process layouts are needed to support the development of robust and close-carbon neutral technologies.
The transformation of high moisture containing biomass to synthetic materials is a challenging task from the thermochemical point of view. Atmospheric conversion technologies require input raw materials with low moisture content (<5 wt%), which makes necessary the utilisation of high energy-consuming drying steps. Hydrothermal gasification (HTG) gained close attention because wet organic and inorganic feedstocks (biomass (Macrì et al., 2020), waste (Su et al., 2020), plastics (Bai et al., 2020)) can be converted in a process into value-added products. Fuel gas composition can be influenced by process parameters (temperature, pressure, feedstock-to-water ratio), homogeneous (Adar et al., 2020) and heterogeneous (Abdpour and Santos, 2021) catalysts. The high flexibility and in-process controllability of hydrothermal conversion can be used to improve waste and biomass valorisation pathways, including renewable hydrogen production (Chen et al., 2019b) or Power-to-Gas applications (Fózer et al., 2020). In order to analyse biomass-to-methanol upgrading via HTG, a detailed and accurate model-based representation of supercritical water gasification is required. Yukananto et al. (2018) developed a computational fluid dynamic model for the supercritical water gasification (SCWG) of glycerol, where the highest error of the model was 16%. Authors dos Santos and Pereira (2021) used a thermodynamic mathematic model to describe the gasification of liquid biomass in supercritical water. Okolie et al. (2020) developed a statistical model using response surface methodology to model the hydrothermal gasification of cellulose, hemicellulose and lignin. However, the accurate and detailed investigation of hydrothermal gasification (including optimisation, examining the main and interacting effects of independent process variables) and the synthesis of low-carbon emission chemicals using hydrothermal technology are limited by the lack of available descriptive HTG models. The limitations of available models are that (i) they involve only the investigation of a few specific model feedstocks, (ii) the independent variables and experimental conditions are examined in narrow intervals, (iii) reactor specifications are not considered directly and (iv) the error of models can be high.
The accurate modelling of biomass decomposition at high temperature and pressure conditions is a complicated task due to the high number of parallel occurring reactions, e.g., hydrolysis, decomposition, hydrogenation, deamination, decarboxylation, C-C breaking, dealkylation, hydroxylation, etc. (Wei et al., 2021). The benefit of machine learning is the ability to make fast and accurate predictions for non-linear computational tasks compared to already available techniques (such as density functional theory) (Csányi et al., 2020). The utilisation of artificial neural networks (ANN) -a sub-discipline of machine learning -is already demonstrated in chemical (Tai et al., 2020) and environmental-related (Poznyak et al., 2019) applications. The ANN was inspired by the biological neural network that can be used to describe complex and/or non-linear relationships. The application of neural networks has been discussed in the field of thermochemical conversions, including the co-pyrolysis of rice husk and sewage sludge (Naqvi et al., 2019), atmospheric biomass gasification (Cerinski et al., 2020), waste tyre blends (Ozonoh et al., 2020) and organic food waste (Gonçalves Neto et al., 2021).
The life cycle assessment (LCA) of methanol production have been performed analysing the environmental performance of various process configurations (Gautam et al., 2020) and feedstocks (e.g., wood (Yadav et al., 2020), rice straw (Im-orb and Arpornwichanop, 2020), coal  and natural gas ). The GHG footprint of conventional methanol production is considered to be high ranging between 0.54 (Pérez-Fortes et al., 2016) and 3.56 kg CO 2,eq (kg MeOH) − 1 (Qin et al., 2016). The involvement of renewable sources was found to be beneficial to decrease the environmental damages of methanol synthesis. Chen et al. (2019c) performed an LCA on an integrated atmospheric biomass gasification cycle and obtained negative GHG emission (− 109.2 kg CO 2,eq (kg MeOH) − 1 ). Adnan and Kibria (2020) concluded that Power-to-Methanol pathways offer climate benefits with negative carbon emission values (ranging from − 325 to − 654 kg CO 2,eq (kg MeOH) − 1 ) using variable renewable solar energy for the conversion. Atmospheric thermochemical valorisation processes and methanol synthesis routes are already demonstrated in the literature as it is discussed above but there is a lack of knowledge considering the environmental impacts and viability of hydrothermal conversion based biomethanol production.
This work investigates the sustainability of a novel biomethanol production alternative using hydrothermal gasification in the process chain. Multiscale computational simulations are carried out incorporating machine learning, process flowsheeting and life cycle and cost analyses. Artificial neural networks are constructed and applied for the simulation of catalytic and noncatalytic thermochemical conversions of various biomass and waste feedstocks. A valorisation pathway for high moisture containing microalgae biomass is proposed involving the integration of fluctuating renewable energy. The environmental performance and GHG footprint of in-situ and external renewable H 2 generation strategies are investigated. It is obtained that the hydrothermal gasification-based biomass-to-methanol upgrading is characterised by low climate change impacts and enhanced decarbonisation properties compared to conventional technologies.

Novel approach and updated methods
The flowchart of the computational framework and simulation methodology is presented in Fig. 1. The missing elements of life cycle inventory (composition and properties of streams, thermochemical performance, reaction characteristics, heat integration possibilities) were determined by conducting process modelling and synthesis that had been supported by supervised machine learning. The flowsheeting was performed by ASPEN Plus v11 software (AspenTech, 2020). The thermodynamic properties were calculated by the Predictive Soave-Redlich-Kwong (PSRK) equation of the state method. The biomethanol production stages, boundaries and limitations are described in Section 2.1.

Water electrolysis
Water electrolysisas the cornerstone of Power-to-X applicationsis a disruptive technology regarding the energy industry. The most advanced methods are alkaline electrolysis (AEL), Polymer Electrolyte Membrane (PEM) and Solid Oxide electrolysis (SOEL). The specific energy consumption of these technologies ranges between 3.7 and 6.5 kWh (Nm 3 H 2 ) − 1 (Buttler and Spliethoff, 2018). For consisting of the energy balance of biomethanol production, the alkaline type electrolyser was considered in the calculations with an average energy consumption of 4.6 kWh (Nm 3 H 2 ) − 1 .
It was estimated that the biomass had been cultivated in open raceway ponds equipped with paddlewheels. Ammonium nitrate and ammonium nitrate phosphate fertilisers were considered as N and P substrates. Following the cultivation phase, the biomass suspension was pre-concentrated using flocculation and centrifugation and transferred to the thermochemical plant via pipelines.

Thermochemical valorisation of biomass
Hydrothermal gasification was considered for the conversion of high moisture containing aquatic biomass. The gaseous product -HTG fuel gascontains mainly H 2 , CH 4 , CO 2 , CO and C 2+ compounds. The application of a homogeneous catalyst (i.e., sodium hydroxide alkali metal) was investigated in the process chain to improve the conversion of the feedstock, influence the composition of the gas phase and increase H 2 and total gas yields (Kumar et al., 2018). A descriptive artificial neural network was developed for the modelling of hydrothermal conversion, as is detailed in Section 2.3. The designed neural network was used to calculate the product stream properties (fuel gas yield and composition) of the HTG operational unit and to provide data to the flowsheeting simulation stage. The total gas yield (Y GAS (mol kg − 1 ) and carbon conversion ratio (CCR (%)) were determined by Eqs. (1) and (2): where Y GAS,i (mol kg − 1 ) is the yield of the ith gas component, m GAS,j, and m feedstock are the weight of the jth gas component and the feedstock (kg), MW C and MW GAS,j are the molar weights of carbon, and the jth gas component (kg kmol − 1 ), w C,feedstock is the carbon content of the feedstock (wt.%).

HTG fuel gas reforming
Fuel gas reforming was simulated to produce high-quality synthesis gas for methanol synthesis. The biogas upgrading was carried out in two stages. First, pre-reforming was considered for the transformation of C 2+ compounds (Eq. (3)) into a mixture of hydrogen and carbon monoxide. In the second step, tri-reforming of methane was taken place that is the combination of (i) partial oxidation (POX) (Eq. (4)), (ii) steam reforming (SRM) (Eq. (5)) and (iii) dry reforming (DRM) (Eq. (6)) reactions.

Methanol synthesis
Synthesis gas is a versatile feedstock that can be used to produce a wide range of chemical products (e.g., alcohols, hydrocarbons and ethers). CO, CO 2 hydrogenations, and reverse water gas shift reaction (RWGSR) (Eqs. (7)-(9)) were considered for the synthesis of biomethanol. The syngas composition has an important role in achieving high methanol concentration in the product stream. Lerner et al. (2018) reported that a syngas modular of 2 is required to maximise the achievable methanol yield in the process. The synthesis gas modular (M SG , Eq. (10)) was adjusted to reach the ideal level of methanol synthesis by using renewable hydrogen from external sources.
Langmuir-Hinshelwood-Hougen-Watson (LHHW) kinetics was applied for the simulation of MeOH production. The rate equations, kinetic factors, constants for driving force and adsorption terms are given in Equations (11)-(13) and Tables 1-3 (Kiss et al., 2016): where f k is the fugacity of components (Pa), k A , k B , k C are kinetic factors. K A (Pa − 2 ), K B (Pa − 2 ), K C (− ) are the equilibrium constants of reactions, K k is the adsorption equilibrium constant of component k (Pa − 1 ), where k equals to H 2 , H 2 O, CO, CO 2 .

Environmental evaluation
The environmental screening of biomethanol production was evaluated by performing a cradle-to-gate life cycle assessment (LCA) according to ISO 14040 and 14044 standards. SimaPro 9.1.1.1 software (PRé Sustainability, 2020) was used to perform LCAs where the life cycle inventory was compiled based on (i) the Ecoinvent v3.4 database, (ii) literature data, (iii) ASPEN Plus v11 and (iv) MATLAB R2020a (Math-Works, 2020) simulation results. The life cycle inventory is summarised in Table 4. Life cycle impact assessment was carried out using the IMPACT2002+ v2.14 method. The investigated cradle-to-gate life cycle system boundary, the operational units, utilities and additional elements are illustrated in Fig. 2. The functional unit of LCA was 1 t of produced biomethanol.

Cost estimation
Economic analysis is conducted to estimate the costs of the HTGbased biomethanol production plant. The Marshall and Swift (M&S) indexation method and ASPEN Process Economic Analyzer v11 (APEA, 2021) software tool were used to determine the cost of equipment. Cost functions and parameters are summarised in Table 5. The total annual cost (TAC) was calculated by Eq. (14): where C OPEX is the operation expenditure (€ y − 1 ), C CAPEX is the Table 1 LHHW kinetic factors for reaction rate expressions (Eqs. (11)-(13)). k is the preexponential factor, E a is the activation energy. annualised capital expenditure (€ y − 1 ) defined by Eq. (15): where CRF is the capital recovery factor (− ), TPC is the total plant cost (€). CRF was determined based on Eq. (16): The plant lifetime (N) and rate of interest (R i ) were assumed to be 25 years and 5%.

Artificial neural network (ANN)
ANN had a long period of intensive development (Klemeš and Ponton, 1992) and also various alternatives were developed (Ponton and Klemeš, 1993). After an assessment Feed-forward backpropagation (FFBP) machine learning algorithm was applied for the modelling of biomass hydrothermal conversion. A Multilayer Perceptron (MLP) consists of input, output and hidden layers. The input layer takes in input data (independent variables), and the output layer provides computed target values (dependent variables). The computational process is carried out in the hidden layer(s) (Elsheikh et al., 2019). Systematic backpropagation can be used to improve the performance of a neural network, i.e., minimising the mean squared error of outputs. The benefit of the FFBP method is that it enables fast and flexible modelling using only the input variables without requiring additional parameters (Ye and Kim, 2018). The training and evaluation of artificial neural networks (ANNs) were carried out using MATLAB R2020a (MathWorks, 2020) software.

Adsorption term
Expressed form Preexponential factor (a i )

Table 5
Parameters of biomethanol cost estimation.

Cost element Functions and parameters Source
The purchase cost of the heat exchanger (PC HX ) (17) F m = Material correction factor. It is 3.75 for stainless steel. F d = Design related correction factor. 0.80 for fixed tube sheet. F p = 0.55 at 6.9 MPa. Extrapolation (R 2 = 0.959) was used based on Douglas (1988) to determine its value at 10 MPa (0.93), 26 MPa (2.56) and 27.5 MPa (2.72).
Area of HX: AHX (m 2 ) = Q ΔT⋅β Q = heat duty (kW); ΔT = temperature difference (− ); β = heat transfer coefficient (kW (K m 2 ) − 1 ) Kharlampidi et al. (2021) The installed cost of the heat exchanger (IC HX )  (Mantingh and Kiss, 2021) The installed cost of the compressor (IC COMP ) Douglas (1988) The purchase cost of the reactor The value of F z is estimated to 16.36 at 26 MPa and 18.11 at 27.5 MPa using polynomial extrapolation (R 2 = 0.993) based on Douglas (1988). F x = material correction factor for pressure vessels, F x = 3.67 in the case of stainless steel.
The installed cost of the reactor (IC R ) Levenberg-Marquardt (LM) and Bayesian Regularisation (BR) algorithms were applied for the training of ANNs. The LM method generally requires less training time, while the BR algorithm is suitable to reach good generalisation in the case of small training data sets. Input data were randomly allocated between model training (70%), validation (15%) and testing (15%). The performance and accuracy of ANNs model predictions were assessed based on mean squared error (MSE) (Eq. (23)) and coefficient of determination (R 2 ) (Eq. (24)).
where Y z pred,j is the predicted, Y z exp,j is the experimental, Y z exp,j is the average of all factor level(s) of the zth target variable, and n is the number of data.
The limitations of the developed neural network are in line with the training data set. The HTG process is highly affected by the reactor configuration, operational conditions, type of feedstock and applied catalyst. In this study, ANNs are developed to describe plug-flow tubular reactor systems for the conversion of organic feedstocks. The boundary and applicability of the machine learning model are in the 380-717 • C, 22.5-34.4 MPa, 1-30 wt% biomass-to-water ratio, 0-5.5 wt% CSR with NaOH catalyst load, and 0.3-60.0 min residence time intervals.

Simulation of hydrothermal gasification with neural networks
Twenty artificial neural network topologies are designed, trained and analysed for the modelling of catalytic and noncatalytic HTG operational units. The examined networks consist of 10 input and 6 target variables. The NN's performance and accuracy are consecutively developed by changing the number of hidden layers and neurons. The examined topologies, performance and accuracy indicators are summarised in Tables 6 and 7. Satisfactory training results are achieved for 1 hidden layer ANN configurations by applying the Bayesian Regularisation backpropagation method. Raising the number of neurons from 5 to 10 is obtained to be a suitable way to improve both the BR and LM training performances. On the other hand, the testing coefficients of correlations are found to be inadequate in the case of single hidden layer topologies. Adding more neurons (10+) to the ML models has negative effects on validation and testing performances.
Expanding the ANN structure with an additional hidden layer results in significant modelling improvement regarding the mean squared errors and coefficient of correlations. It is obtained that this topology change contributes to reaching high training and testing performances (MSE < 1) by running either the Levenberg-Marquardt or Bayesian Regularisation algorithms. In the case of multiple hidden layers, better testing performances are achieved with the LM backpropagation method. The number of neurons in the hidden layers is adjusted to reduce the mean squared error of the validation process.
The ideal ANN topology for the HTG thermochemical process is illustrated in Fig. 3. It is determined that using 2 hidden layers with 17 neurons in each layer outperforms other topology alternatives. The results show that the LM-10-17-17-6-6 ANN structure is characterised by the best overall training (MSE = 5.680E-01 and R = 0.9822) (Fig. 4a), validation (MSE = 8.249E-01 and R = 0.9974) (Fig. 4b), and testing (MSE = 4.597E-01 and R = 0.9935) (Fig. 4c) performances and accuracies. The developed ideal neural network topology is used to predict the hydrothermal conversion of Chlorella vulgaris microalga biomass, improve HTG synthesis gas quality and develop the process synthesis of biomethanol production.

Process synthesis for biomass-to-methanol transformation
The process flowsheeting diagram of biomethanol production is presented in Fig. 5. The simulation results and stream properties are summarised in Tables S1-S6. Aquatic Chlorella vulgaris eukaryotic green algae strain is used for the biofixation of carbon dioxide. Following the cultivation phase, the pre-concentrated wet biomass is converted into a fuel gas mixture using hydrothermal gasification. The aim of the thermochemical conversion stages is the production of high-quality synthesis gas that can be suitably valorised further to a low-carbon intermediate synthetic platform material, i.e., methanol.
The hydrothermal gasification of biomass results in a (1) gas mixture that contains hydrogen, methane, carbon dioxide, carbon monoxide, longer (C 2+ ) alkane and alkene chains and (2) process water with dissolved organic compounds. The constructed artificial neural network enables the detailed investigation of the relationships between the elemental composition of biomass feedstocks, thermochemical process parameters and achievable fuel gas yields. The simulation of catalytic hydrothermal gasification was implemented in the ASPEN Plus process flowsheeting software by applying a yield-type reactor combined with the outputs of the LM-10-17-17-6-6 artificial neural network model. The effects of HTG process parameters on the gas yield and carbon conversion ratio are illustrated in Fig. 6. Damergi et al. (2019) reported that high HTG temperature levels (up to 600 • C) are required in the absence of catalysts to achieve adequate biomass conversion with a carbon conversion efficiency of 41-43%. Our study shows that the carbon conversion ratio and the total gas yield can be increased above 50% and 39 mol kg − 1 by elevating the temperature from 550 • C to 700 • C (Fig. 6a) at ideal pressure levels (Fig. 6c). The temperature also has a positive effect on methane selectivity, but lower temperature levels are The results show that low biomass-to-water levels (≤5 wt%) improve the thermochemical process performance indicators, and BWR has an important role in achieving simultaneously high CCR, gas yields, and hydrogen selectivity (Fig. 6b). The effect of BWR was also confirmed by Leong et al. (2021) who indicated that lower feedstock concentration improves gasification efficiency. Fig. 6c demonstrates that an elevated H 2 evolution rate can be attained by carrying out biomass transformation in a pressure range of 25-27 MPa.
A similar tendency can be described in the case of catalytic hydrothermal conversion, where the H 2 yield can be increased by 29.8% using 1.5 wt% NaOH catalyst load at 625 • C and 27.5 MPa (Fig. 7a). Figs. 6c and 7b and suggest that there is an interaction between pressure and catalyst concentration factors regarding the carbon conversion ratio and H 2 yield. The utilisation of homogeneous catalysis shifts the ideal pressure interval to 28-30 MPa at 625 • C and 12.5 wt% BWR. Fig. 7c shows that residence time has minor effects on the process performance indicators, but lower factor settings are preferred.
Improving and adjusting the hydrothermal conversion is essential for the production of high-quality synthesis gas feedstock. The simulations show that the fuel gas composition, H 2 & CO 2 selectivity and the carbon conversion ratio can be controlled and increased by applying sodium hydroxide homogeneous catalyst, as is illustrated in Fig. 7d. The results suggest that the H 2 yield and CCR can be raised by applying 2 wt% and    2.5 wt% catalyst loads at 625 • C, respectively. These ANN simulation results are in agreement with the pilot-scale findings of Adar et al. (2020), who reported that the H 2 content of fuel gas could be increased significantly by applying 2 wt% KOH catalyst concentration. The interaction between the biomass-to-water ratio and catalyst load is investigated in the case of total fuel gas yield (Fig. 7e) and carbon conversion ratio (Fig. 7f). Conducting the hydrothermal conversion at an elevated 700 • C temperature level shows that the highest carbon conversion with increased gas yield (39.24 mol kg − 1 ) can be achieved at 5 wt% BWR and 1.9 wt% catalyst concentration. Using variable renewable energy (e.g., photovoltaic panels and wind turbines) for clean hydrogen generation plays a key role in low-carbon fuels production and decarbonisation of niche applications. In the current system boundary, the renewable H 2 can be supplied from two sources: (i) as the main component of HTG fuel gas and (ii) external generation involving water electrolysis operational unit. The H 2 yield and synthesis gas selectivity can be influenced during the HTG conversion by (1) applying ideal reaction conditions and (2) homogeneous catalysis. The high flexibility of the hydrothermal valorisation regarding achievable fuel gas composition enables various synthesis gas production scenarios.
In order to achieve adequate synthesis gas feedstock composition defined by the synthesis gas modular (Eq. (10)) and to meet low GHG emission environmental criteria, two main conversions, strategies are distinguished in the flowsheeting process and ex-ante sustainability assessment: (1) boosting in-situ H 2 evolution in the hydrothermal gasification process by applying sodium hydroxide homogeneous catalyst and (2) enhancing the HTG synthesis gas quality with the integration of H 2 from external water electrolysis supply.
Based on the ANN simulation data, two different hydrothermal reaction conditions are selected for the thermochemical conversion of high moisture containing Chlorella vulgaris biomass: (1) catalytic hydrothermal gasification at 700 • C, 27.5 MPa, 5 wt% BWR, 1.9 wt% NaOH catalyst with 2 min residence time in the tubular reactor; and (2) noncatalytic hydrothermal gasification at 700 • C, 26 MPa, 5 wt% BWR, 2 min residence time settings.
Following the biomass transformation, the waste heat content of the high-temperature HTG product stream is recovered in a multi-stage heat exchanger system. The liquid phase and fuel gas products are separated in a phase separator, where the gas mixture is sent to the fuel gas reforming section, and the side product process water is used for additional heat recovery.
The HTG fuel gas mixtures are characterised by low syngas modular (cHTG: M SG = 0.74; HTG: M SG = 0.60) and contain unwanted side products (hydrocarbons). The fuel gas upgrading process involves two major steps: (i) Pre-steam reforming is applied to transform the C 2+ hydrocarbon compounds into synthesis gas. (ii) In the second step, the excess methane content of the gas stream is converted into syngas by using tri-reforming. It is estimated that the required oxygen for partial oxidation is supplied by water electrolysis as a co-product of H 2 production. The pre-and tri-reforming processes are modelled in Gibbs-reactor units. The effects of reforming parameters are examined on the synthesis gas modular, alkane and alkene conversions to enhance the fuel gas upgrading procedure. Sensitivity analyses are performed to determine ideal reforming conditions, as is summarised in Fig. 8. The simulation results demonstrate that high ethane conversion can be achieved by performing pre-reforming above 400 • C, 20 bar and 1 kmol h − 1 steam molar flow rate ( Fig. 8a and b).
The tri-reforming of cHTG fuel gas serves two main goals: 1. The transformation of methane into synthesis gas, and 2. The adjustment of synthesis gas modular close to ideal levels prior to MeOH synthesis.
D. Fózer et al. modular of the S-207 stream. (Fig. 9). Raising steam and oxygen mole flow rates result in high methane conversion (Fig. 9a and c) but augmented oxygen flow rate has negative effects on the value of M SG (Fig. 9b). Fig. 9d presents that the syngas modular can be increased up to 1.75 by adjusting the steam mole flow during the reforming process. It is obtained that the addition of CO 2 does not affect the methane conversion significantly, but it is beneficial to mitigate global warming effects and to fine-tune the level of syngas modular. Fig. 9e illustrates that limiting the CO 2 mole flow rate to an upper constraint of 10 mol h − 1 contributes to reaching optimal synthesis gas composition prior to methanol production. The simulation results highlight that a trade-off has to be made between reaching effective methane reforming or the ideal level of synthesis gas modular. It is obtained that maintaining elevated methane conversion results in 1.45 and 1.37 M SG values in the cases of catalytic and noncatalytic HTG fuel gases. For this reason, the resulted product stream of tri-reforming is enriched with H 2 to adjust the synthesis gas composition prior to methanol synthesis. The incorporation of water electrolysis in the biomethanol production chain enables the indirect utilisation of surplus variable renewable electricity produced by wind turbines and photovoltaic panels. In this way, the proposed biomethanol technology can be developed into a Power-to-Liquid decarbonisation process.
The methanol synthesis phase involves multi-stage syngas pressurisation, a continuous reactor with a recirculation system and methanol purification units. The feedstock gas stream is pressurised in 3 stages up to 10 MPa and is fed to a boiling water reactor where three kinetic LHHW reactions are considered: (i) The hydrogenation of carbon monoxide (Eq. (7)) (ii) The hydrogenation of carbon dioxide (Eq. (8)) and (iii) Reverse water-gas shift reaction (Eq. (9)).
The unreacted gaseous reagents are stripped and recirculated to the syngas feed. Recycling the unreacted reagents to the reformed syngas product reduces the synthesis gas modular to a value of 1.72. Additional hydrogen is mixed into the syngas feed to achieve the ideal syngas composition for methanol synthesis (where M SG equals approximately 2), as is detailed in Fig. 10a and b.
Sensitivity analyses were performed to maximise methanol yield. It is determined that the highest methanol mole fraction (x CH3OH = 0.17) can be realised at 190 • C (Fig. 10c) using 0.50-0.70 kmol h − 1 H 2 mole flow rate (Fig. 10d). Finally, the produced methanol and water are separated in a distillation column to obtain a methanol rich stream with high purity (x CH3OH > 0.99).

Cost estimation of biomethanol production
The total annual cost distribution is illustrated in Fig. 11. The water electrolysis has the highest TAC share reaching 61.1% and 59.2% for the catalytic and noncatalytic HTG cases. The TAC of the hydrothermal conversion process was obtained to be 7.3% higher for the catalytic case. The fuel gas reforming and methanol synthesis stages are also characterised by a higher TAC share compared to the noncatalytic HTG alternative peaking at 1.4% and 2.0%. The total annual costs for the catalytic and noncatalytic process alternatives are obtained to be 316 US$ (t MeOH) − 1 and 339 US$ (t MeOH) − 1 .  investigated the production costs of conventional coal-to-methanol processes and obtained values between 264.0 and 272.6 US $ t − 1 . The economic aspect of methanol production from natural gas was evaluated by Blumberg et al. (2019) involving various reforming technologies. The levelized cost of methanol was ranged between 198 and 295 US$ t − 1 . Bellotti et al. (2019) investigated the economic sustainability and feasibility of methanol synthesis using hydrogen and sequestered CO 2 . The calculated methanol production cost was assumed to be 324 € t − 1 (381 US$ t − 1 ) using renewable energy sources in the plant. (Yang et al. (2018)) examined a biomass-to-methanol process alternative using dual-stage entrained flow gasification for the production of bio-syngas. The biomass-to-methanol production cost was in the range of 302-336 US$ t − 1 .
The results of cost estimation indicate that the production cost of methanol via hydrothermal gasification is in the same range as reported in the case of other biomass-to-methanol scenarios. The economic analysis shows that the utilisation of catalysts in the HTG process is beneficial to reduce the installed equipment cost of hydrothermal conversion by 10.0%. The overall feasibility of biomethanol production could be improved by applying more effective catalysts in the HTG process, and the forthcoming rise in carbon tax (Stevens and Carroll, 2020) could also influence the production costs and margins positively.

Life cycle impact assessment of power-to-biomethanol alternatives
Multi-perspective sustainability assessments are conducted to quantify the environmental damages of biomass-to-methanol scenarios. According to the intermediate hydrogen generation approach, the life cycle impacts of (1) in-situ cHTG H 2 boosting and (2) the utilisation of augmented supply from external sources (i.e., water electrolysis) are investigated as separate process design and LCA scenarios.
The specific greenhouse gas emission of biomethanol alternatives is illustrated by subprocesses in Fig. 12. It is obtained that significant GHG emission reduction can be achieved by both process configurations in a cradle-to-gate system boundary. The utilisation of sodium hydroxide catalyst is beneficial to improve the hydrothermal conversion of biomass; however, it also elevates the attributed environmental impacts. In the case of the catalytic process, the highest emission rates are coupled with the hydrothermal gasification process (410 kg CO 2,eq (t CH 3 OH) − 1 ) followed by the alkali metal catalyst production (257 kg CO 2,eq (t CH 3 OH) − 1 ). The rest of the processes can be described with a cumulated emission rate of 279 kg CO 2,eq (t CH 3 OH) − 1 . Using microalgae strain for the biofixation of CO 2 results in high specific carbon emission uptake (-1,372 kg CO 2,eq (t CH 3 OH) − 1 ). As it is presented in Fig. 12a, the overall GHG footprint of the catalytic biomass valorisation and methanol production amounts to − 439 kg CO 2, eq (t CH 3 OH) − 1 . Fig. 12b shows that the noncatalytic hydrothermal gasification can be described with a GHG emission rate of 470 kg CO 2,eq (t CH 3 OH) − 1 that is followed by the methanol synthesis process (126 kg CO 2,eq (t CH 3 OH) − 1 ), water electrolysis (72 kg CO 2,eq (t CH 3 OH) − 1 ), tri, and prereforming of fuel gas (84 and 18 kg CO 2,eq (t CH 3 OH) − 1 ). The cultivation of Chlorella vulgaris results in a CO 2 absorption value of 1,500 kg CO 2,eq (t CH 3 OH) − 1 . The overall greenhouse gas footprint of the LCA alternative is obtained to be − 725 kg CO 2,eq (t CH 3 OH) − 1 . Nguyen et al. (2021) investigated the combination of dry reforming and partial oxidation of methane for the production of methanol and attained a CO 2 emission of 810 kg CO 2 (t CH 3 OH) − 1 . Eggemann et al. (2020) examined the global warming potential of 9 Power-to-Fuel systems producing methanol from waste and obtained climate change mitigation potentials between − 5.48 and 0.22 kg CO 2,eq (kg methanol) − 1 . The LCA results of the present study confirm that low carbon emission synthesis of biomethanol is achievable by converting aquatic biological resources to fuel gas and synthesis gas. Applying catalytic and noncatalytic hydrothermal gasification in the conversion chain open up possibilities by enabling the direct transformation of high moisture containing biomass and waste into value-added products and lowering the greenhouse gas effects of synthetic materials and fuels production.
The life cycle characterisation factors of biomethanol production scenarios are listed by sub-processes in Table 8 and Table 9. The results indicate that fuel gas production and biofixation of CO 2 are environmental bottlenecks in a cradle-to-gate framework. The highest global warming effect (70.1%) is associated with the thermocatalytic conversion of biomass. Supercritical water gasification is also identified as the main contributor to the respiratory inorganics (55.3%) and respiratory organics (42.7%) emission categories ( Table 7). The multipurpose environmental screening demonstrates that global warming impacts can be neutralised by involving photosynthetic carbon capture in methanol production. As a side effect, the biofixation of CO 2 elevates terrestrial acidification, where its midpoint share amounts to 52.9%.
The noncatalytic HTG process is rated as one of the major contributors to the overall environmental impacts. Hydrothermal gasification is responsible for 60.1% of the total global warming potential when biomass valorisation is combined with increased external H 2 supply ( Table 9). The high GWP of supercritical water gasification was also   11. Total annual cost distribution of biomethanol production using catalytic and noncatalytic hydrothermal gasification.
D. Fózer et al. suggested in former studies in the cases of corn stalks (Wang et al., 2019) and coal (Chen et al., 2019a). In addition, it is obtained that the fuel gas production stage has the highest share regarding aquatic eutrophication (43.8%), terrestrial ecotoxicity (43.7%) and mineral extraction (42.7%) damage categories. The biomass cultivation stage has a negative overall global warming potential because of the high CO 2 utilisation rate during   the metabolism of photosynthetic eukaryotic microalgae cells. On the other hand, the production and utilisation of fertilisers induce significant environmental impacts on terrestrial acidification (56.9%), application of non-renewable energies (48.4%), aquatic acidification (44.5%) and ozone layer depletion (42.5%) characterisation factors. The mid-and endpoint environmental impacts of biomethanol production alternatives are illustrated in Fig. 13. The highest life cycle impacts are coupled with the emission of respiratory inorganics and noncarcinogen compounds, the utilisation of non-renewable energy, global warming potential and terrestrial ecotoxicity (Fig. 13a). The performed sustainability assessments demonstrate that the in-situ H 2 boosting strategy tends to be a more favourable LCA scenario maintaining negative climate change effects and achieving lower damages on human health and ecosystem quality by 2.7% and 10.6% (Fig. 13b). Increasing the external H 2 utilisation results in significantly better decarbonisation potentials, where the climate change effect and resources utilisation can be decreased by 65.5% and 10.4%.
The modelling of the HTG process with an artificial neural network showed that the catalytic upgrading of biomass is a suitable method to enhance H 2 selectivity, carbon recovery and fuel gas yield. The improved thermochemical conversion decreases the required amount of biomass feedstock by 9.1% to 1.452 t algae (t MeOH) − 1 and, as an after effect, the utilisation of fertilisers. This conversion-related benefit results in lower environmental burdens on human health and ecosystem quality endpoint subfactors (Fig. 13b). The application of sodium hydroxide homogeneous catalyst elevates climate change impacts, and the endpoint damages of biomethanol production by 78.6 IMPACT 2002+ mPt (t MeOH) − 1 that is 14.5% of the total impacts. The high specific global warming potential of alkali catalyst utilisation calls the attention for developing and screening applicable catalysts for hydrothermal conversions considering both reaction performance indicators and environmental criteria.
The performed ex-ante sustainability assessments highlight that combining the biofixation of CO 2 and flexible fuel gas production are advantageous technological pairing in decarbonisation applications. The biomass-to-methanol valorisation via hydrothermal gasification is characterised by a negative greenhouse gas footprint indicating strong CO 2 removal and low-emissions carbon fuel production potentials. The integration of controlled biogas generation into the Power-to-Fuel process schemes enables effective biomass valorisation, the production of low-carbon synthetic fuels and materials and the indirect reduction of GHG emissions.

Conclusions
The biomethanol is a valuable intermediate product, a platform molecule that can be used for the production of low-carbon emission fuels and materials. The sustainability of biomethanol production is evaluated by applying machine learning, process flowsheeting and life cycle assessment computational tools. Process synthesis is carried out by transforming high moisture containing microalgae biomass into synthesis gas and biomethanol in sequential thermochemical steps. Artificial neural network (ANN) models are developed for the simulation of catalytic and noncatalytic hydrothermal gasification unit operations. Ex-ante cradle-to-gate life cycle assessments indicate that strong decarbonisation potentials can be achieved by involving the biofixation of CO 2 and hydrothermal valorisation in the Power-to-Liquid conversion chain. Hydrogen generation strategies have an important role in achieving close carbon-neutral emission rates. The in-process flexibility of hydrothermal gasification and the use of renewable hydrogen contribute to decreasing the GHG footprint of biomethanol production by 65.1% and the overall endpoint impacts by 6.2%. The process economic analysis shows that the production cost of biomethanol via hydrothermal gasification and fuel gas upgrading ranges between 316 and 339 US$ (t MeOH) − 1 depending on the applied hydrogen supply strategy. The biomethanol production based on supercritical water gasification offers an attractive option for (i) carbon dioxide utilisation, (ii) valorisation of biomass and (iii) decarbonisation of conventional processes.

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
This work was supported by the ÚNKP-20-4-II-BME-296 new national excellence program of the ministry for innovation and technology from the source of the national research, development and innovation fund and the NTP-NFTÖ-20-B-0095 National Talent Program. The authors are grateful for the financial support from the Hungarian National Scientific Research Foundation (OTKA) projects: nr.: 128543 and nr.: 131586. The research reported in this paper and carried out at BME has been supported by the NRDI Fund (TKP2020 NC, Grant No. BME-NC) based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry for Innovation and Technology. The research was supported by the EU LIFE program, LIFE-CLIMCOOP project (LIFE19 CCA/HU/001320 Mole fraction of methane (− ) Y GAS Total gas yield (mol kg − 1 ) Z i Mole fraction of H 2 , CO 2 , CO (− )