Techno-Economic and Life Cycle Assessment of Hydrogen Production from Biomass–Plastic Co-Gasification with Carbon Capture and Storage

Abstract

This study evaluates the techno-economic and environmental feasibility of hydrogen (H₂) production via co-gasification of woody biomass and polyethylene (PE) plastic waste, with and without carbon capture and storage (CCS), using an integrated modeling framework. Four scenarios were analyzed: (1) biomass gasification without CCS, (2) biomass with CCS, (3) co-gasification (90:10 biomass:PE) without CCS, and (4) co-gasification with CCS. Process simulations were conducted in Aspen Plus V12.1, techno-economic analysis (TEA) via NREL’s H₂A model, and cradle-to-gate life cycle assessment (LCA) in OpenLCA with TRACI 2.1 and the Cumulative Energy Demand (CED) methods. The plant processes 1500 dry ton/day feedstock, yielding ~136–140 tons/day pure H₂. TEA results show co-gasification without CCS achieves the lowest levelized cost of H₂ (LCOH) at 2.18 USD/kg, 7% below biomass-only (2.34 USD/kg), due to reduced feedstock demand and improved efficiency. CCS increases LCOH by 30–36% (2.98–3.18 USD/kg), but 70 USD/t CO₂ credits reduce it to 1.74–1.81 USD/kg, competitive with gray H₂. Sensitivity and Monte Carlo analyses highlight capacity factor and feedstock as key drivers, with co-gasification narrowing uncertainties. LCA reveals co-gasification lowers most impacts by 5–10%, while CCS enables net-negative GWP (−12.3 to −14.7 kg CO₂ eq/kg H₂) but raises CED by 15%. Scenario 4 balances economic viability and climate mitigation, supporting circular economy principles through waste valorization. Findings affirm co-gasification with CCS as a promising pathway for low-carbon H₂, with policy incentives critical for deployment. Future optimizations, like higher PE ratios, could further reduce costs below 2 USD/kg, advancing net-zero transitions.

1. Introduction

Hydrogen (H₂) is increasingly recognized as a versatile energy carrier essential for achieving global decarbonization goals, particularly in hard-to-abate sectors such as heavy industry, long-haul transportation, and chemical manufacturing [1]. Global hydrogen demand reached more than 97 Mt in 2023 and could reach almost 100 Mt in 2024, driven primarily by traditional uses in refining and ammonia production, though low-emission hydrogen remains a small fraction at about 1 Mt annually [2]. Projections indicate that to align with net-zero emissions by 2050, hydrogen demand could surge to 530 Mt by mid-century, with low-carbon pathways needing to scale rapidly to meet climate targets set by the Paris Agreement [3]. This escalating demand underscores the urgency for sustainable production methods that minimize greenhouse gas emissions while addressing resource constraints.

In parallel, the circular economy paradigm offers a framework for transforming waste streams into valuable resources, reducing reliance on virgin materials and mitigating environmental pollution [4]. Biomass residues, such as forestry waste, and non-recyclable plastics, which constitute a significant portion of municipal solid waste (over 350 Mt of plastic produced annually, with less than 10% recycled globally), represent underutilized feedstocks that can be valorized through thermochemical processes like gasification [5,6,7,8]. Co-gasification of biomass and plastic waste not only diverts materials from landfills—reducing methane emissions and marine pollution—but also promotes synergies in syngas production, enhancing hydrogen yields through complementary chemical compositions (e.g., biomass’s oxygen content aiding plastic cracking) [9]. Integrating carbon capture and storage (CCS) in such processes can further enable “blue” or “negative-emission” hydrogen, aligning with circular principles by sequestering biogenic CO₂ and closing carbon loops [10]. This approach supports sustainable development goals, fostering energy security, waste management, and climate mitigation in a resource-constrained world.

While biomass gasification for hydrogen production is well-established, with efficiencies reaching 50–70% in commercial systems, the co-processing with plastic waste remains underexplored, particularly in integrated frameworks combining techno-economic analysis (TEA) and life cycle assessment (LCA) [10]. Existing studies demonstrate that co-gasification can boost H₂ selectivity by 10–20% due to plastic’s high H/C ratio, but most focus on experimental kinetics or standalone gasification, neglecting downstream CCS integration and holistic sustainability evaluations [9,11]. For instance, while CCS has been applied to biomass gasification achieving net-negative emissions (−15 to −22 kg CO₂ eq/kg H₂), its application to mixed biomass–plastic feeds is limited, with gaps in assessing energy penalties, cost escalations, and environmental trade-offs like increased toxicity from plastic additives [10]. Recent reviews highlight challenges in scaling co-gasification, including tar formation, feedstock heterogeneity, and the lack of comprehensive models evaluating CCS under varying ratios, yet few address U.S.-specific contexts with policy incentives like 45Q credits [12,13]. Moreover, while TEA and LCA for biomass-to-H₂ with CCS exist, integrated assessments for co-gasification are scarce, often overlooking probabilistic uncertainties and scenario-based comparisons essential for deployment viability [14]. This study bridges these gaps by simulating realistic co-gasification scenarios with CCS, providing multi-faceted insights into economic competitiveness and environmental performance.

The primary objective of this study is to evaluate the techno-economic and environmental feasibility of hydrogen production via co-gasification of woody biomass and polyethylene (PE) plastic waste, with and without CCS, to inform sustainable pathways for low-carbon H₂. Specific aims include: (1) developing a rigorous Aspen Plus simulation model validated against benchmarks to assess process performance; (2) conducting TEA using NREL’s H₂A model to quantify levelized costs, sensitivities, and uncertainties; (3) performing cradle-to-gate LCA via OpenLCA and TRACI 2.1 to analyze midpoint impacts and hotspots; and (4) identifying optimization levers for deployment in circular economy contexts.

2. Methodology

This study employs a comprehensive modeling framework to evaluate sustainable H₂ production via co-gasification of woody biomass and PE plastic waste, integrated with optional CCS. Four scenarios were systematically analyzed: (1) baseline woody biomass gasification without CCS, (2) baseline woody biomass with CCS, (3) co-gasification of 90% woody biomass and 10% PE without CCS, and (4) co-gasification with CCS. All simulations were conducted using Aspen Plus V12.1 (Aspen Technology, Inc., Bedford, MA, USA), a rigorous process simulator widely validated for biomass thermochemical conversion processes. The TEA was performed using the National Renewable Energy Laboratory’s (NREL, Golden, CO, USA) H₂A Central Production Model [15], while LCA utilized the TRACI 2.1 impact assessment method [16] implemented in OpenLCA V1.16 [17]. The plant capacity was scaled to process 70,000 kg/h of raw feedstock, consistent with commercial-scale biomass-to-H₂ facilities [10]. All economic parameters were adjusted to 2016 USD with a 10% internal rate of return and 30-year plant life, following NREL guidelines [15]. LCA boundaries encompassed cradle-to-gate (feedstock acquisition to H₂ delivery), with system allocation based on mass and energy content.

2.1. Feedstock Characterization

Feedstock properties are critical for accurate simulation of gasification kinetics, syngas composition, and downstream processing efficiency. Two feedstocks were characterized: woody biomass (Pinus radiata chips, representative of forestry residues) and low-density polyethylene (LDPE) plastic waste. Table 1 summarizes the proximate, ultimate, and physical properties of both feedstocks. The properties of woody biomass were obtained from standardized ultimate and proximate analyses, whereas the properties of PE were derived from ASTM D3801 specifications for recycled plastics [18]. Both datasets were compiled from the literature and represent average values reported in multiple publications [19,20,21]. The co-gasification scenario utilized a 90:10 mass ratio of woody biomass to PE, reflecting realistic waste stream compositions in municipal solid waste gasification.

Table 1. Proximate, Ultimate, and Physical Properties of Woody Biomass and Polyethylene (PE) Feedstocks.

Property	Woody Biomass	Polyethylene (PE)
Proximate Analysis (wt%)
Moisture	10.0	0.0
Volatile Matter	75.5	0.0
Fixed Carbon	22.5	100.0
Ash	2.0	0.0
Ultimate Analysis (wt%, dry basis)
Carbon (C)	50.0	85.6
Hydrogen (H)	6.25	14.4
Nitrogen (N)	0.2	0.0
Oxygen (O)	41.5	0.0
Sulfur (S)	0.05	0.0
Chlorine (Cl)	0.0	0.0
Ash	2.0	0.0
Sulfur Forms (wt%)
Pyritic	0.0	0.0
Sulfate	0.0	0.0
Organic	0.05	0.0
Particle Size Distribution
Size Range (mm)	0.5–2.0	<2.0 (shredded)
Weight Fraction per Interval	0.2 (uniform)	0.2 (uniform)
Higher heating value (HHV, MJ/kg)	19.5	41.4

The nonconventional components (WOODY, PE, CHAR, ASH, WOODY-DR) were defined in Aspen Plus using the HCOALGEN and DCOALIGT property methods, with enthalpy correlations based on Perry’s Chemical Engineers’ Handbook [22]. Conventional components utilized the Soave–Redlich–Kwong (SRK) [23] equation of state, validated against experimental syngas data (±5% deviation). DEPG (dimethyl ether of polyethylene glycol) was specified as a pseudocomponent for Selexol solvent in CCS scenarios, with binary interaction parameters from Aspen Physical Property Database.

2.2. Process Modeling and Simulation

The integrated hydrogen production process was rigorously simulated in Aspen Plus V12.1 using a steady-state flowsheet model, validated against experimental and commercial benchmarks for biomass gasification and downstream processing. The simulation framework comprised five interconnected stages: (1) feedstock pre-processing and drying, (2) two-stage pyrolysis–gasification, (3) water–gas shift (WGS) reaction, (4) pressure swing adsorption (PSA) for H₂ purification, and (5) optional Selexol-based CO₂ capture for CCS scenarios (Scenarios 2 and 4). All scenarios processed 70,000 kg/h of raw feedstock at ambient conditions (25 °C, 1 bar), with an equivalence ratio (ER) of 0.25 and steam-to-carbon ratio (S/C) of 0.9, optimized for maximum H₂ selectivity [10,20]. Property methods included SRK for conventional gases and HCOALGEN/DCOALIGT for nonconventional solids [24]. The Peng–Robinson equation of state with the Boston–Mathias alpha function (PR–BM) was employed for vapor-phase thermodynamics, as commonly applied in biomass gasification modeling [25]. Heat and mass balances were closed to 99.9% accuracy, with convergence achieved via Wegstein method (tolerance: 10⁻⁶) [26]. Figure 1 illustrates the comprehensive process flowsheet, highlighting modular integration of carbon capture. Key operating parameters and reaction kinetics are detailed below.

Figure 1. Process flowsheet for hydrogen production via biomass/plastic co-gasification with optional carbon capture and storage (CCS) Integration.

Raw feedstock entered the DRYER (RStoic block, 90% moisture removal efficiency) at 25 °C and 1 bar, exiting at 120 °C with 10% vapor fraction. The VAP-SEP (Flash2 block) separated the vapor (vented) from dry solids (WOODY-DR, 00% solid phase), preventing agglomeration in downstream units. For co-gasification (Scenarios 3–4), shredded PE (10% mass) was pre-mixed with woody biomass using a stoichiometric calculator.

The dried solid was then fed to a high-temperature pyrolysis reactor (PYRO, modeled as a plug-flow reactor at 450 °C), which instantaneously decomposed the biomass into gaseous volatiles, tar and a solid char fraction. The pyrolysis products were split by a separator (SEP-1) into (i) a vapor stream rich in H₂, CO, CO₂, CH₄ and H₂O (“PYRGAS”) and (ii) a solid stream of char + ash. The PYRGAS stream (pure vapor phase) was routed to the downstream gasifier, while the char solids (pure C + ash) were directed to further gasification.

In the gasification stage, air and steam were injected to partially oxidize and gasify the char. The PYRGAS, char, steam and air were mixed (MIXER) and sent first to a combustion reactor (COMBUST) at ≈800 °C. In the combustor (modeled as a plug-flow oxidation reactor), oxygen reacts with the char and pyrolysis gases, producing heat and partially oxidizing carbon. This step raised the gas temperature and generated a flue gas containing CO, H₂, unburned CO₂ and residual O₂. The hot combustor effluent then entered a second reduction reactor (REDUCT, also plug-flow at ~800 °C) where steam reforming and gasification reactions took place under fuel-rich conditions (no added O₂). In the REDUCT block, the remaining carbon was fully gasified, driving key water–gas and Boudouard reactions.

The kinetic model employs a Langmuir–Hinshelwood (L–H) formulation to represent the heterogeneous char reactions, capturing the competitive adsorption and surface reaction behavior among gaseous species (CO, CO₂, H₂O, and H₂) on the char surface. The overall gasification mechanism incorporates oxidation, reforming, and heterogeneous reduction reactions, as summarized in Table 2. In this model, gas-phase reactions such as methane partial oxidation and steam reforming are described by power law rate expressions, while solid–gas reactions (C + H₂O, C + CO₂, and C + 2H₂) follow L–H kinetics with an adsorption denominator accounting for surface coverage effects. The kinetic parameters—including the pre-exponential factors and activation energies applied in different scenarios—are presented in Table 3.

Table 2. Key Gasification, Oxidation, and Reforming Reactions with Rate Expressions Used in the Simulation Model.

ID	Reaction Equation	Rate Expression *	Notes
Rox1	$\mathrm{C}\mathrm{O}+0.5{\mathrm{O}}_2\leftrightarrow {\mathrm{C}\mathrm{O}}_2$	$r_{Rox1}=k_{Rox1}{p}_{CO}^{\alpha }{p}_{O_2}^{\beta }$	Gas-phase/heterogeneous oxidation (fast). α, β typically 1.
Rox2	${\mathrm{C}\mathrm{H}}_4+0.5{\mathrm{O}}_2\leftrightarrow \mathrm{C}\mathrm{O}+2{\mathrm{H}}_2$	$r_{Rox2}=k_{Rox2}{p}_{{CH}_4}^{\gamma }{p}_{O_2}^{\delta }$	Partial oxidation of methane (produces H2).
Rred1	$\mathrm{C}+{\mathrm{H}}_2\mathrm{O}\leftrightarrow \mathrm{C}\mathrm{O}+{\mathrm{H}}_2$	$r_{Rred1}=k_1{a}_C{\displaystyle \frac{{pH}_{2}O-\left(pCO\right)\left({pH}_2\right)/p°}{1+\sum K_i{p}_i}}$	Langmuir–Hinshelwood form for char steam gasification (heterogeneous).
Rred2	${\mathrm{C}\mathrm{H}}_4+{\mathrm{H}}_2\mathrm{O}\leftrightarrow \mathrm{C}\mathrm{O}+3{\mathrm{H}}_2$	$r_{Rred2}=k_{SR}{p}_{{CH}_4}^m{p}_{H_{2}O}^n$	Steam methane reforming (gas-phase/catalytic on char).
Rred3	$\mathrm{C}\mathrm{O}+{\mathrm{H}}_2\mathrm{O}\leftrightarrow {\mathrm{C}\mathrm{O}}_2+{\mathrm{H}}_2$	$r_{Rred3}=k_f{pCOpH}_2-k_{b}p{CO}_{2}p{H}_2$	Modeled as forward/backward with equilibrium constant $K_{eq}$.
Rred4	$\mathrm{C}+{\mathrm{C}\mathrm{O}}_2\leftrightarrow 2\mathrm{C}\mathrm{O}$	$r_{Rred4}=k_2{a}_C{\displaystyle \frac{{pCO}_2-p^{2}CO/p°}{1+\sum K_i{p}_i}}$	Boudouard reaction (heterogeneous).
Rred5	$\mathrm{C}+2{\mathrm{H}}_2\leftrightarrow {\mathrm{C}\mathrm{H}}_4$	$r_{Rred5}=k_m{a}_C{\displaystyle \frac{p^2{H}_2-{pCH}_4/p°}{1+\sum K_i{p}_i}}$	Usually minor under gasification conditions; included for closure.

* $p_i$: partial pressure of species i (bar). Langmuir–Hinshelwood adsorption/surface reaction denominator $1+\sum K_i{p}_i$ includes competitive adsorption of CO, CO₂, H₂O, H₂ on char. $a_C$: active surface site concentration (mol active site per kg char), required for heterogeneous rates. $p°$: standard pressure (1 bar). For the WGS (Rred3) we supplied forward rate and used $k_b=k_f/K_{eq}\left(T\right)$, so the equilibrium was satisfied at each temperature.

Table 3. Pre-Exponential Factors and Activation Energies for Kinetic Parameters in Scenarios 1–2 and 3–4 [20,25,27].

ID	Scenarios 1 and 2		Scenarios 3 and 4
	Pre-Exponential A (Nominal)	Activation Energy E (J·mol−1)	Pre-Exponential A (Nominal)	Activation Energy E (J·mol−1)
Rox1	1.0 × 106 (bar−1.s−1)	8.0 × 104	1.05 × 106 (bar−1.s−1)	8.0 × 104
Rox2	5.0 × 106 (bar−1.s−1)	1.05 × 105	5.75 × 106 (bar−1.s−1)	1.05 × 105
Rred1	2.25 × 105 (s−1.site−1)	1.22 × 105	1.69 × 105 (s−1.site−1)	1.22 × 105
Rred2	1.0 × 108 (bar−1.s−1)	1.20 × 105	1.25 × 108 (bar−1.s−1)	1.20 × 105
Rred3	2.0 × 105 (bar−2.s−1)	7.5 × 104	2.10 × 105 (bar−2.s−1)	7.5 × 104
Rred4	5.35 × 102 (s−1.site−1)	7.74 × 104	4.75 × 102 (s−1.site−1)	7.74 × 104
Rred5	6.2 × 10−2 (s−1.site−1)	1.92 × 104	8.3 × 10−2 (s−1.site−1)	1.92 × 104

After the gasification stage, the hot product gas was cooled and sent to two water–gas shift reactors in series (HTWGS at 350 °C, then LTWGS at 250 °C) to further convert CO to H₂. These were modeled as equilibrium reactors (implicitly achieving ≥98% CO conversion in the low-T stage) following standard WGS chemistry. In our simulation, the HT-WGS converted roughly 27% of CO (increasing H₂ and CO₂), and the LT-WGS removed 95% of remaining CO (driving H₂ higher). Condensation and flashing then removed most excess water, yielding a dry syngas stream.

The dry syngas was pressurized and cooled (COMP-1, COOLER4) before Pressure Swing Adsorption (PSA) purification. The PSA unit was represented as an ideal separator (PSA-SEP) delivering a high-purity H₂ product and a CO₂/N₂-rich tail gas. These PSA assumptions follow typical off-the-shelf PSA performance. The small hydrogen slip was assumed recoverable as fuel gas.

Finally, for Scenarios 2 and 4 the PSA tail gas was sent to a CO₂ capture unit. A Selexol (DEPG) absorber was modeled at 25 bar and 25 °C, absorbing CO₂ from the compressed tail gas. The rich solvent was then flashed and stripped (HEATER, STRIPPER) to release a nearly pure CO₂ stream, which was assumed stored or sequestered. Under the chosen conditions, the absorber removed over 90% of the inlet CO₂. The treated “clean” syngas leaving the absorber contained the remaining H₂ and light inerts (mostly N₂).

2.3. Techno-Economic Assessment Framework

Techno-economic analysis was conducted to evaluate the economic viability of the four hydrogen production scenarios, integrating process data from Aspen Plus to derive the key metrics such as capital expenditure (CAPEX), operating expenditure (OPEX), and levelized cost of hydrogen (LCOH). The analysis utilized the NREL’s H₂A Central Production Model (Version 3.2018) [15], a standardized Discounted Cash Flow (DCF) tool developed for consistent assessment of hydrogen pathways under U.S. Department of Energy guidelines. Aspen Plus outputs, such as mass/energy balances, H₂ yields, utility demands, and CO₂ emissions, were directly imported into H₂A to ensure fidelity between simulation and economics. For CCS scenarios, the H₂A Carbon Sequestration module was activated to account for compression, transport, and injection costs, assuming 90% capture efficiency, with or without a 70 USD/metric ton CO₂ credit [10,15].

The plant was scaled to a design capacity of 1500 metric tons/day dried feed. All costs were expressed in 2016 USD (H₂A basis year), with sensitivity to inflation (1.9%) and escalation factors. Financial assumptions included a 3-year construction period, 1-year startup, 30-year plant life, partial equity financing, a 10% after-tax internal rate of return, and a 25.7% combined tax rate. Working capital equaled 15% of annual OPEX changes, with decommissioning and salvage values each at 10% of total capital investment (TCI). Capital costs were derived from Aspen Plus equipment sizing and scaled using standard cost correlations. Indirect costs covered site preparation, engineering/design, contingencies, and permitting. Fixed operating costs included labor, general and administrative expenses, taxes, insurance, and maintenance, while variable costs comprised feedstock, utilities, waste treatment, and materials. For CCS scenarios, the H₂A Carbon Sequestration module was employed to estimate the additional costs, assuming a 90% CO₂ capture efficiency, 101.3 kPa outlet pressure, 75% pump efficiency, and 160 km pipeline transport distance for geologic storage. Sequestration assumptions included a ground temperature of 12 °C and CO₂ density of 866 kg m⁻³ calculated using the Peng–Robinson equation of state (PR EOS) [28].

Table 4 summarizes the financial parameters and cost inputs used in the DCF model. Feedstock and utility prices were set to 2016 values (woody biomass 0.0996 USD/kg, waste PE assumed to have no cost or credit, natural gas 7.77 USD/MMBtu, electricity 0.1043 USD/kWh), with consumption rates obtained from the Aspen simulation. Labor and fixed O&M were estimated from H2A defaults (54 FTE, 58 USD/man-h burdened labor, 20% G&A) and other variable costs (e.g., waste treatment, disposal) were included. These inputs and assumptions are representative of current biomass-to-H₂ plant economics and ensure consistency across all four scenarios. LCOH was calculated using Equation (1) [28]:

$$LCOH={\displaystyle \frac{{\sum }_{t=0}^n\frac{{CAPEX}_t+{OPEX}_t+{Taxes}_t}{{(1+r)}^t}}{{\sum }_{t=1}^n\frac{H_2{Production}_t}{{(1+r)}^t}}}$$

(1)

where r is the real discount rate (10%). Uncertainty was quantified via Monte Carlo simulation (1000 iterations), assuming triangular distributions for key inputs [28].

Table 4. Financial Assumptions, Startup Costs, and Carbon Sequestration Parameters for the Discounted Cash Flow Model.

Parameter	Value
Financial Assumptions
Operating Capacity Factor (%)	90.0
Plant Design Capacity (kg H2/day)	43,795
Reference/Startup/Basis Year	2016
Construction Period (years)	3
Capital Spending (% Year 1/2/3)	8/60/32
Startup Time (years)	1
Plant Life (years)	30
Depreciation Schedule (years)	20 (MACRS)
Equity Financing (%)	40
Debt Interest Rate (%)	3.70
Debt Period (years)	10
Startup Costs (% Fixed/Revenue/Variable)	100/50/75
Decommissioning (% Depreciable Capital)	10
Salvage Value (% Total Capital)	10
Inflation Rate (%)	1.9
After-Tax Real IRR (%)	10.0
Total Tax Rate (%)	25.74
Working Capital (% Δ Operating Costs)	15
Carbon Sequestration (CCS Scenarios)
CO2/kg H2 (Scenario 2, kg/kg H2)	20.97
CO2/kg H2 (Scenario 4, kg/kg H2)	20.13
Capture Efficiency (%)	90

As a by-product of the process, the CO₂ price must be considered when calculating the real levelized cost of hydrogen. Carbon pricing mechanisms include emission trading systems, carbon taxes, and carbon offsets [29]. In the U.S., for instance, the 45Q tax credit provides financial incentives for CCS, with the value varying by project type [30]. Globally, carbon prices differ according to regional policies and carbon programs. Given this variability, the present TEA evaluated a range of carbon prices reported in the literature to assess their influence on project economics. This helps guide business and investment decisions by estimating the financial impacts of carbon pricing, identifying potential revenue or risk areas, and testing the implications of climate policies on investment performance. Beyond geological storage, CO₂ with a purity above 99.5% can be marketed as an industrial gas; however, geological sequestration is more beneficial for long-term climate mitigation and net carbon removal. Various carbon capture and utilization pathways have been analyzed in prior research and were therefore excluded from this study [31].

2.4. Life Cycle Assessment Methodology

Life cycle assessment was performed to quantify the environmental impacts of the four hydrogen production scenarios, adhering to the ISO 14040/14044 standards [32]. The analysis followed a cradle-to-gate boundary, encompassing feedstock acquisition, transportation, gasification, WGS, PSA, and optional CCS, excluding downstream H₂ distribution and use to focus on production-phase hotspots. This scope aligns with well-to-gate TEA boundaries while enabling attributional LCA for comparative scenario evaluation [33]. Functional unit was defined as 1 kg of >99.8% pure H₂ produced, facilitating normalized impact comparisons. System allocation was based on mass for multi-output processes (e.g., syngas intermediates) and energy content for co-products (e.g., captured CO₂), with sensitivity to economic allocation explored.

The LCA was implemented in OpenLCA V1.16 (GreenDelta GmbH, Berlin, Germany), an open-source platform supporting rigorous inventory modeling and multi-impact assessment [17]. Process-specific foreground data, such as mass balances, energy demands, and emissions, were extracted from converged Aspen Plus models and scaled to the functional unit. Background data for upstream processes (e.g., biomass harvesting, PE waste collection, electricity grid mix) were sourced from the Ecoinvent 3.8 database (cutoff system model), customized for U.S. regional contexts to reflect forestry residues and municipal waste streams. For CCS scenarios, CO₂ sequestration was modeled as geologic storage with 99% permanence over 1000 years, including compression (0.12 kWh/kg CO₂) and pipeline transport (100 miles), per NETL baselines [28].

Impact assessment utilized the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI 2.1) method, developed by the U.S. Environmental Protection Agency for North American contexts and encompassing eight midpoint categories: ozone depletion (kg CFC-11 eq), global warming (kg CO₂ eq), acidification (kg SO₂ eq), eutrophication (kg N eq), photochemical smog formation (kg O₃ eq), human health carcinogenics (CTUh), human health non-carcinogenics (CTUh), and ecotoxicity (CTUe). To address energy resource depletion, the Cumulative Energy Demand (CED) method (V1.11) was incorporated, quantifying total primary energy use (MJ eq) across fossil, and renewable sources [34]. No normalization or weighting was applied to maintain midpoint transparency, with results aggregated per functional unit and verified for uncertainty based on ±10% variability in key inventories.

3. Results and Discussion

3.1. Process Simulation Validation and Performance Metrics

The Aspen Plus simulation results provide critical insights into the performance of the hydrogen production process across the four scenarios, with a focus on syngas quality, conversion efficiencies, and the impacts of plastic co-feeding and CCS integration. Model validation against literature benchmarks ensures reliability, while detailed analysis highlights scenario-specific advantages. Figure 2 illustrates key performance metrics for Scenarios 1 and 3, offering a visual comparison of syngas composition, yields, ratios, and efficiencies, with uncertainty quantified via Monte Carlo simulation.

Figure 2. Comparative analysis of hydrogen production scenarios. Panel (A): syngas mass fractions (wt%) for H₂, CO, CO₂, CH₄. Panel (B): yields and ratios including H₂/CO (mol/mol), syngas yield (Nm³/kg feed), H₂ yield (wt% of feed). Panel (C): efficiency and consumption metrics including carbon conversion (%), electricity consumption (kWh/kg H₂), thermal consumption (MJ/kg H₂), energy efficiency (HHV basis, %). Panel (D): Monte Carlo uncertainty analysis for Scenario 1 on H₂ yield, energy efficiency, and carbon conversion (%).

For the baseline woody biomass scenario (Scenario 1), the simulated syngas composition (H₂: 10.1 wt%, CO: 57.69 wt%, CO₂: 22.73 wt%, CH₄: 8.74 wt%) aligns closely with reported values for air steam gasification of pine residues at 800 °C and ER = 0.25, where H₂/CO ratios range 2.0–2.6 and CO₂ content 20–25 wt% [20,35,36]. This consistency stems from the model’s accurate representation of endothermic steam reforming and partial oxidation reactions, which favor CO production while limiting CO₂ through Boudouard equilibrium under fuel-rich conditions. Syngas yield (1.8 Nm³/kg feed) and carbon conversion (72%) match pilot-scale dual-bed gasifiers (1.5–2.0 Nm³/kg, 70–80% conversion) [37,38], attributable to the Langmuir–Hinshelwood kinetics capturing char surface limitations. Energy efficiency (51.4% HHV basis) is consistent with thermodynamic models for similar systems (50–70%) [39], reflecting heat integration between exothermicity in combustion and endothermicity in reduction stages.

For co-gasification (Scenario 3), incorporating 10% PE increased H₂ yield to 10.4 wt% of feed and energy efficiency to 53.6%, corroborating studies on biomass–PE blends showing 4% yield enhancements due to PE’s high H/C ratio and catalytic cracking [13,40,41,42]. The improved performance arises from synergistic radical interactions, where PE-derived hydrocarbons promote biomass devolatilization and suppress tar formation, enhancing gas-phase reforming. Electricity and thermal consumptions (1.84 kWh/kg H₂ and 12.6 MJ/kg H₂) are within 5–10% of validated Aspen models for integrated gasification, owing to reduced endothermic demands from PE’s volatile content. CCS scenarios (2 and 4) introduced a 5–8% energy penalty from compression (0.12 kWh/kg CO₂) and solvent regeneration (0.85 GJ/t CO₂), aligning with NETL benchmarks for Selexol processes (0.8–1.0 GJ/t CO₂) [43,44]. This penalty reflects the thermodynamic cost of solvent stripping but is mitigated by heat recovery from WGS exothermicity.

Syngas composition post-gasification varied significantly between scenarios, reflecting feedstock influences on reaction pathways. In Scenario 1, the H₂/CO molar ratio was 2.53, favorable for WGS enhancement. This profile results from dominant partial oxidation and steam reforming, where biomass’s oxygen content promotes CO₂ formation via water–gas shift, balancing H₂ selectivity. Co-feeding 10% PE in Scenario 3 elevated H₂ to 10.4 wt% and CO to 59 wt%, while reducing CO₂ to 20 wt%, attributable to PE’s hydrocarbon cracking boosting H₂ selectivity via enhanced steam reforming [27]. The shift arises from PE’s low oxygen, suppressing CO₂ pathways and favoring methanation intermediates that decompose to H₂, underscoring PE’s role as a hydrogen donor and tar suppressant.

Hydrogen yield post-PSA reached 10.1 wt% of feed in Scenario 1 and 10.4 wt% in Scenario 3, with PSA recovery >80% in both, consistent with zeolite-based adsorbents for biomass syngas [45]. The improvement in Scenario 3 stems from higher initial H₂/CO ratios and reduced impurities, facilitating better PSA selectivity and lower purge losses. CCS integration (Scenarios 2 and 4) minimally affected upstream composition but reduced net H₂ yield by 1–3% due to parasitic loads from CO₂ compression, highlighting the trade-off between purity and energy efficiency, as solvent regeneration diverts thermal resources from process heating [46]. Figure 2A,B) visually contrasts these metrics, demonstrating co-gasification’s superiority in H₂ enrichment through synergistic feedstock interactions.

Carbon conversion and energy metrics further elucidate process performance. Scenario 1 achieved 72% carbon conversion, with electricity and thermal consumptions of 1.91 kWh/kg H₂ and 13.7 MJ/kg H₂, yielding 51.4% HHV-based efficiency—comparable to optimized air-blown gasifiers (60–70%) [10]. This efficiency reflects effective heat recovery in dual-stage gasification, where combustion exothermicity supports endothermic reduction, though limited by biomass’s high ash content impeding char reactivity. Co-gasification in Scenario 3 improved conversion to 74%, reduced electricity to 1.84 kWh/kg H₂ and thermal to 12.6 MJ/kg H₂, and boosted efficiency to 53.6%, driven by PE’s volatile content enhancing char porosity and reaction kinetics, thereby minimizing unreacted carbon and parasitic pumping/compression loads [11].

CCS scenarios incurred an additional 0.4–0.6 MJ/kg H₂ penalty from solvent regeneration and compression, lowering efficiencies by 4–6% but enabling >90% CO₂ capture. The penalty arises from the thermodynamic requirements of pressure swing and thermal stripping in Selexol, offset partially by WGS heat integration but underscoring the need for advanced solvents or membranes to mitigate [46]. Figure 2C illustrates these efficiencies, with Panel D’s Monte Carlo analysis (1000 iterations, ±10% variability) confirming robustness: mean H₂ yield 10.1 wt% (SD 1.02), energy efficiency 51.4% (SD 6.54), and carbon conversion 72% (SD 7.2) for Scenario 1. Such uncertainty quantification, often overlooked in biomass studies, underscores the model’s reliability under variable inputs like feedstock heterogeneity or operational fluctuations [47].

Co-feeding PE (Scenarios 3 and 4) enhanced overall performance by 5–10% across metrics, mitigating biomass limitations like high oxygen content and improving gasification kinetics through synergistic interactions (e.g., PE-derived radicals promoting biomass devolatilization and suppressing tar polymerization). This aligns with recent pilot trials reporting 8–15% efficiency gains in co-gasification, driven by increased volatile release and reduced activation energies for char–steam reactions [48]. CCS integration (Scenarios 2 and 4) achieved 96.7% CO₂ recovery with minimal H₂ loss (<0.5%), but increased parasitic loads (e.g., 106.9 MJ/t CO₂ for sequestration) necessitating heat integration (e.g., WGS exotherm recovery) to maintain viability. The net effect is a balanced trade-off: CCS enables carbon negativity but demands optimized compression efficiencies to avoid >5% yield penalties, as seen in similar CCS systems [46]. Comparative flowsheet analysis revealed co-gasification with CCS (Scenario 4) as optimal for low-carbon H₂, reducing net emissions by 85–95% while maintaining competitive yields through PE’s role in enhancing syngas calorific value.

3.2. Techno-Economic Results and Cost Breakdown

Techno-economic analysis reveals that co-gasifying woody biomass with PE plastic waste significantly reduces the levelized cost of hydrogen (LCOH) by improving process efficiency and lowering feedstock consumption. In contrast, integrating CCS increases costs because of higher capital and energy demands, though it also enables carbon credit revenues. Among the four simulated scenarios, co-gasification without CCS (Scenario 3) achieved the lowest LCOH of 2.18 USD/kg H₂, while biomass gasification with CCS (Scenario 2) reached the highest LCOH of 3.18 USD/kg H₂. Co-gasification with CCS (Scenario 4) provided a balanced outcome at 2.98 USD/kg H₂, and its cost could drop below 1.8 USD/kg H₂ if CO₂ credits reach 70 USD/ton. These values position the processes competitively against fossil-based H₂ (1.5–2.5 USD/kg) when carbon credits are factored, though higher than DOE targets (<2 USD/kg by 2030) due to biomass supply chain costs [49]. Figure 3 illustrates the comparative cost structures.

Figure 3. Breakdown of levelized cost of hydrogen (LCOH, USD/kg H₂) across four scenarios, with 70 USD/ton CO₂ credits applied to CCS cases (Scenarios 2 and 4).

In Scenario 1 (baseline biomass without CCS), the levelized cost of hydrogen (LCOH) was estimated at 2.34 USD/kg. The feedstock cost was the dominant contributor, accounting for 47.6% (1.11 USD/kg) of the total. This high share stems from the substantial biomass consumption (10.96 kg of feedstock per kg H₂) and its moderate unit price (0.0996 USD/kg), reflecting the reliance on woody residues with relatively low energy density (0.0196 GJ/kg). Other variable costs contributed 18.4% (0.43 USD/kg), primarily due to utility requirements such as natural gas (0.013 MMBtu/kg H₂ at 7.77 USD/MMBtu) and electricity (1.9 kWh/kg H₂ at 0.104 USD/kWh). These utilities are mainly consumed in gas compression and pumping operations within the gasification and water–gas shift (WGS) stages. Capital (13.7%, 0.32 USD/kg) and fixed operation and maintenance (O&M) costs (11.1%, 0.26 USD/kg) were relatively moderate, benefiting from the technological maturity and scalability of fluidized bed gasification systems. Overall, feedstock expenses dominate the cost structure—nearly half of the total—while electricity and steam utilities together account for about 20%. The moderate capital cost underscores the advantage of employing well-established gasification technology in the baseline configuration.

In Scenario 2 (biomass with CCS), the levelized cost of hydrogen (LCOH) increased to 3.18 USD/kg, representing a 36% rise compared with the baseline case. This escalation was primarily attributed to the capital (27.9%, 0.89 USD/kg) and variable (19.6%, 0.62 USD/kg) costs associated with CCS implementation. The sequestration process alone accounted for approximately 0.45 USD/kg H₂ (equivalent to 40.10 USD per ton CO₂, excluding capture), driven by the energy demand for CO₂ compression (0.12 kWh/kg CO₂) and additional operation and maintenance (O&M) costs. Although minor electricity credits from CO₂ compression partially offset these expenditures, the overall results emphasize the thermodynamic and economic penalties of CCS, particularly in low-pressure biomass systems. Despite the integration of CCS, the feedstock contribution decreased to 35% of the total LCOH, reflecting relative cost dilution rather than reduced consumption, as the absolute biomass demand remained high.

While CCS integration substantially increases the production cost of hydrogen, the application of policy incentives can meaningfully offset this economic penalty. In particular, the U.S. 45Q tax credit [30], which offers 50–100 USD per ton of CO₂ sequestered, has the potential to significantly reduce the effective cost of biomass-based hydrogen production with carbon capture. When carbon credits of approximately 70 USD per ton CO₂ are incorporated in the scenario 2, the effective LCOH decreases to around 1.81 USD/kg H₂, bringing biohydrogen production costs into direct competition with gray hydrogen. This underscores the critical role of carbon pricing and policy frameworks in enhancing the economic viability of low-carbon hydrogen pathways.

In Scenario 3 (co-gasification without CCS), the LCOH decreased to 2.18 USD/kg, representing a 7% reduction compared with the baseline biomass-only case (Scenario 1). This improvement was primarily driven by lower feedstock costs (46.2%, 1.01 USD/kg), owing to reduced biomass demand (9.9 kg biomass per kg H₂) and the assumption of zero-cost PE, which was treated as a waste-derived feedstock under a gate fee model [11]. The co-processing of biomass and PE effectively leveraged waste streams to create economic synergies and enhance conversion performance. Variable costs declined to 17.7% (0.38 USD/kg), reflecting 7.3% lower natural gas consumption (0.01 MMBtu/kg H₂) and 3.7% less electricity usage (1.84 kWh/kg H₂). These reductions stem from the higher heating value of PE (0.046 GJ/kg), which improved gasification efficiency and reduced auxiliary power requirements [11]. While capital (14.5%) and fixed O&M (11.8%) shares remained comparable to those of the baseline system, the overall techno-economic performance of Scenario 3 was notably stronger. The cost reduction highlights the synergistic benefits of co-gasifying biomass with plastic waste, as PE enhances cracking and gasification reactions, leading to better hydrogen yields and energy efficiency. Consequently, this configuration offers the lowest hydrogen production cost among all cases, reinforcing its potential for waste-abundant regions seeking both economic and environmental gains through integrated resource utilization.

In Scenario 4 (co-gasification with CCS), the LCOH was 2.98 USD/kg, representing a 6% decrease relative to the biomass-only CCS case (Scenario 2). This configuration effectively balances the cost-saving benefits of co-feeding with the additional expenses of CCS. Although capital costs increased to 28.5% (0.85 USD/kg), reflecting the added CCS infrastructure, the sequestration cost was relatively moderate at 0.43 USD/kg H₂ (or 41.70 USD per ton CO₂), as higher hydrogen yields diluted fixed investments and improved overall process utilization. Variable costs accounted for 19.2% (0.57 USD/kg), capturing the combined influence of co-gasification efficiency and CCS energy requirements. The inclusion of PE improved syngas quality, thereby reducing CO₂ compression loads and partially mitigating the energy penalty typically associated with carbon capture. The system’s design capacity of 157,746 kg H₂/day further distributed both capital and operational costs, contributing to the lower overall LCOH compared with biomass-only configurations. Despite CCS inherently increasing capital and energy demands, the co-gasification route demonstrates superior cost efficiency, with sequestration expenses remaining within international cost ranges (30–50 USD per ton CO₂). Importantly, incorporating carbon credit mechanisms such as 45Q or equivalent incentives (70 USD per ton CO₂) could reduce the effective LCOH to 1.74 USD/kg H₂, positioning this pathway as competitive with gray hydrogen while maintaining a low-carbon footprint.

Co-gasification (Scenarios 3 and 4) outperforms biomass-only processes by lowering LCOH through improved conversion efficiency and reduced reliance on biomass supply chains. While CCS adds roughly 0.8–1.0 USD/kg H₂, its inclusion enables net-negative emissions and long-term sustainability, particularly if carbon markets mature. Feedstock blending also improves economic resilience by reducing exposure to biomass price volatility and supply limitations. For early-stage plants with 50% higher capital costs, the LCOH could rise by 1.3–1.5×, highlighting the importance of scale-up and nth-plant deployment for cost reduction.

3.3. Sensitivity, Uncertainty, and Comparative Economic Analysis

Sensitivity analysis elucidates the robustness of the techno-economic model by quantifying LCOH response to variations in key inputs, revealing critical levers for cost optimization. In Scenario 1 (biomass without CCS), LCOH exhibited highest sensitivity to feedstock consumption (±5%, ΔLCOH = 0.113 USD/kg), as biomass comprises 47.6% of costs and is subject to supply chain volatility (e.g., harvesting, logistics) that amplifies price swings. Operating capacity factor (±5%, ΔLCOH = 0.083 USD/kg) ranked second, reflecting biomass plants’ susceptibility to downtime from fouling or seasonal feed availability, which reduces utilization and spreads fixed costs over fewer units [50]. Utilities and capital showed moderate impacts, driven by energy-intensive compression and mature gasification technology minimizing capital risks.

In Scenario 4 (co-gasification with CCS), capacity factor (±5%, ΔLCOH = 0.125 USD/kg) emerged as most sensitive, exacerbated by CCS’s high fixed costs (e.g., sequestration 106.9M USD), where underutilization dilutes credit revenues and amplifies depreciation burdens. Capital (±5%, ΔLCOH = 0.065 USD/kg) ranked second, as CCS adds 20–30% to TCI through specialized equipment like absorbers/strippers, vulnerable to construction escalations. Fixed O&M and utilities followed, with CCS compression (0.12 kWh/kg CO₂) heightening energy sensitivity despite PE’s mitigating efficiencies. Feedstock (±5%, ΔLCOH = 0.015 USD/kg) had minimal impact, as co-feeding reduces usage (9.9 kg/kg H₂ vs. 10.96), buffering price risks through waste PE’s low/negative costs. These sensitivities highlight the importance of optimizing plant operations and capital planning in CCS-integrated systems, as corroborated by recent studies on biomass gasification with CO₂ capture [51]. Figure 4a,c depicts these tornado plots, showing CCS shifts sensitivity from variables to fixed/capital, as sequestration introduces inelastic overheads less affected by operational tweaks.

Figure 4. Sensitivity and uncertainty analysis of LCOH. Panels (a,c): tornado plots showing LCOH sensitivity to key inputs (±5% variation) for Scenario 1 (biomass without CCS) and Scenario 4 (co-gasification with CCS). Panels (b,d): Monte Carlo probability distributions for LCOH (USD/kg H₂) in Scenarios 1 and 4 (1000 iterations, triangular distributions ±20%).

Monte Carlo simulation (1000 iterations, triangular distributions ±20%) further probed probabilistic risks. For Scenario 1, LCOH centered at 2.34 USD/kg with 80% probability between 2.30–2.38 USD USD/kg, indicating tight distribution driven by predictable utilities but wider tails from feedstock uncertainty (±15% LCOH impact at extremes). High-probability bins (e.g., 23% at 2.32–2.33 USD/kg) reflect model stability under nominal conditions, but outliers (>2.40 USD/kg) highlight vulnerability to supply disruptions. Scenario 4 showed broader spread (80% between 1.70–1.80 USD/kg, mean 1.74 USD/kg), with CCS amplifying tails due to sequestration’s fixed costs, where low capacity (86%) spikes LCOH to 1.80 USD/kg. Figure 4b,d visualizes these probabilities, underscoring co-gasification’s narrower uncertainty (SD ±8% vs. ±12%), as PE diversifies inputs and stabilizes yields. This methodology is consistent with recent applications of Monte Carlo simulations in evaluating uncertainties for supercritical water biomass gasification systems [52].

Waterfall charts decompose optimistic adjustments. In Scenario 1, baseline 2.34 USD/kg reduced to 2.20 USD/kg with −5% feedstock/utilities/capital/O&M, primarily from feedstock (0.06 USD/kg drop), as lower consumption leverages economies in handling. Capacity +5% added 0.04 USD/kg saving through better utilization. Scenario 4’s baseline 1.74 USD/kg fell to 1.61 USD/kg, with capacity +5% yielding 0.06 USD/kg reduction, emphasizing CCS’s leverage on fixed assets where uptime maximizes credit offsets. Figure 5a,b illustrates these cumulates, showing co-gasification’s greater relative gains from variables, as PE enhances flexibility.

Figure 5. Waterfall charts of optimistic adjustments to baseline LCOH. Panel (a): Scenario 1 (biomass without CCS) and panel (b): Scenario 4 (co-gasification with CCS).

Comparatively, co-gasification tempers CCS penalties by 20–25%, as higher yields dilute fixed sequestration costs. Reasons include PE’s role in boosting H₂/CO ratios, reducing downstream loads and enabling compact designs. However, without credits, CCS remains a premium (30–36% LCOH increase), driven by energy thermodynamics—compression alone consumes 10–15% plant power, resolvable via advanced materials or renewable integration. Global carbon pricing (50–100 USD/ton) could invert this, dropping Scenario 4 LCOH to 1.5–2.0 USD/kg, competitive with gray H₂ while achieving negativity. Comparative studies on mixed plastic waste gasification with CCS support these findings, demonstrating potential environmental and economic benefits [53].

These analyses affirm co-gasification’s economic edge through resource efficiency and waste synergy, with CCS viability hinging on markets. For deployment, prioritizing low-cost PE sourcing and heat-optimized CCS could achieve <2 USD/kg, supporting net-zero transitions. Future refinements, like higher PE ratios or amine hybrids, merit exploration to minimize penalties.

3.4. Environmental Impacts and Life Cycle Assessment Results

The LCA quantifies the environmental performance of the four hydrogen production scenarios, revealing substantial reductions in global warming potential (GWP) through CCS while highlighting trade-offs in resource depletion and toxicity from plastic co-feeding. The results underscore the synergistic benefits of biomass–plastic co-gasification in mitigating upstream impacts, driven by enhanced process efficiency and waste diversion, but also emphasize the energy-intensive nature of CCS that amplifies cumulative energy demand (CED). Figure 6 presents the midpoint impacts across all scenarios, demonstrating the transformative role of CCS in achieving carbon negativity. Figure 7a,b illustrates contribution analyses for Scenarios 1 and 4, elucidating shifts in impact sources.

Figure 6. Midpoint environmental impacts across TRACI 2.1 categories and CED (MJ eq/kg H₂) for all four scenarios, demonstrating CCS’s role in achieving net-negative GWP.

Figure 7. Contribution analysis of environmental impacts. Panel (a): Scenario 1 (biomass without CCS) and panel (b): Scenario 4 (co-gasification with CCS).

Across TRACI 2.1 categories, co-gasification (Scenarios 3 and 4) consistently lowered impacts by 5–10% compared to biomass-only (Scenarios 1 and 2), primarily due to higher H₂ yields reducing feedstock and energy demands per functional unit. For ozone depletion (1.1–1.5 × 10⁻⁷ kg CFC-11 eq/kg H₂), electricity generation’s chlorofluorocarbon emissions dominate, with co-gasification’s efficiency gains (e.g., 3.7% less electricity in Scenario 3) yielding marginal reductions, as upstream grid mix remains fossil-heavy in U.S. contexts. Investigations into various biohydrogen production from biomass resources highlight the importance of feedstock selection in minimizing ozone depletion impacts [54]. GWP ranged from 5.8–6.5 kg CO₂ eq/kg H₂ without CCS to −12.3 to −14.7 kg CO₂ eq/kg H₂ with CCS, where sequestration credits outweigh process emissions, enabling net negativity; co-gasification amplifies this by 15–20% through lower biogenic CO₂ per kg H₂ from PE’s non-biogenic carbon.

Acidification (0.041–0.055 kg SO₂ eq/kg H₂) and eutrophication (0.011–0.015 kg N eq/kg H₂) stem from upstream biomass cultivation (fertilizer runoff) and electricity (SOx/NOx from coal/gas), reduced in co-gasification by diluted biomass input, though PE’s production adds minor NOx from cracking. Environmental performance assessments of mixed plastic waste gasification with CCS show net negative climate change impacts, supporting the benefits of co-feeding [53]. Smog formation (0.25–0.34 kg O₃ eq/kg H₂) follows similar patterns, with CCS increasing volatile organic compounds from solvent handling. Human health impacts—carcinogenics (3.8–5.2 × 10⁻⁸ CTUh/kg H₂) and non-carcinogenics (2.1–2.9 × 10⁻⁷ CTUh/kg H₂)—arise from air separation emissions and PE additives (e.g., heavy metals), heightened 10–15% in co-gasification but mitigated by waste diversion avoiding landfill leachate. Techno-economic and LCA studies on mixed plastic waste gasification pathways emphasize trade-offs in human health and toxicity categories [14]. Ecotoxicity (14.4–19.2 CTUe/kg H₂) reflects PE’s upstream petrochemical burdens, increasing 15% in Scenarios 3/4, as plastic monomers contribute to aquatic toxicity. CED (42.8–55.4 MJ eq/kg H₂) rises 15% with CCS from compression (0.12 kWh/kg CO₂), but co-gasification offsets 6–8% via higher efficiencies, emphasizing PE’s role in energy conservation. Feasibility studies on gasifying mixed plastic waste for hydrogen production indicate lower environmental impacts compared to single-stream plastics [27]. These trends highlight co-gasification’s environmental edge in non-CCS scenarios through resource optimization, while CCS transforms GWP but exacerbates energy-related categories, necessitating renewable integration for holistic sustainability.

Contribution analysis identifies impact hotspots, with forest residue dominating Scenario 1 (55–65% across categories) due to cultivation emissions (e.g., fertilizer for acidification/eutrophication), underscoring biomass’s land use burdens despite biogenic credits. Electricity contributes 15–30%, from grid fossil content amplifying smog and toxicity. In Scenario 4, contributions shift: forest residue drops to 30–50% from diluted input, PE waste adds 10–22% to fossil depletion/ecotoxicity via upstream petrochemicals, but sequestration credits −40% GWP by storing biogenic CO₂. CCS itself adds 8–15%, from energy-intensive compression increasing CED/acidification. Air separation (5–10%) and gas cleaning (5–12%) are minor but notable in health impacts from chemical use. Figure 7 visualizes these shifts, showing CCS rebalancing contributions toward downstream processes.

Co-gasification’s reductions (5–10%) stem from PE’s higher HHV minimizing biomass needs and emissions, aligning with circular principles by valorizing waste. CCS’s negativity in GWP (−14.7 kg CO₂ eq/kg H₂ in Scenario 4) arises from 90% capture offsetting 21.8 kg process CO₂/kg H₂, but 15% CED increase highlights energy trade-offs, resolvable via renewables. Ecotoxicity rise (16.9 CTUe in Scenario 4) reflects PE additives, mitigated by cleaner sourcing. Overall, Scenario 4 excels for climate mitigation but requires policy support (e.g., 45Q credits) to offset costs. Future hybrid feedstocks could further optimize, emphasizing CCS’s role in net-zero transitions.

4. Conclusions

This study demonstrates the potential of co-gasification of woody biomass and PE plastic waste as a sustainable route for hydrogen production, particularly when integrated with CCS. Through rigorous Aspen Plus simulations, NREL H₂A techno-economic analysis, and OpenLCA-based LCA, four scenarios were evaluated, revealing co-gasification’s synergies in enhancing process efficiency, reducing costs, and mitigating environmental impacts.

Key findings from the process modeling indicate that co-feeding 10% PE improves H₂ yields by 4–5%, carbon conversion to 74%, and energy efficiency to 53.6%, driven by complementary feedstock interactions that suppress tar and boost syngas quality. CCS integration achieves >90% CO₂ capture but incurs 5–8% energy penalties, underscoring the need for heat recovery optimizations.

TEA results highlight co-gasification without CCS (Scenario 3) as the most cost-effective, with LCOH at 2.18 USD/kg, 7% below biomass-only (2.34 USD/kg), due to reduced biomass demand and zero-cost PE under gate fee models. CCS raises LCOH to 2.98–3.18 USD/kg, but policy incentives like 70 USD/t CO₂ credits lower it to 1.74–1.81 USD/kg, making it competitive with fossil-based H₂. Sensitivity analyses emphasize capacity factor and capital as critical levers in CCS scenarios, while Monte Carlo simulations confirm co-gasification’s narrower uncertainties (SD ±8%), enhancing economic resilience.

LCA quantifies environmental benefits, with co-gasification lowering impacts by 5–10% across TRACI categories through resource optimization. CCS transforms GWP to net-negative (−14.7 kg CO₂ eq/kg H₂ in Scenario 4) by sequestering biogenic CO₂, but increases CED by 15% from compression demands, necessitating renewable energy integration. Trade-offs include heightened ecotoxicity (15%) from PE additives, mitigated by waste diversion avoiding landfill burdens.

Overall, Scenario 4 (co-gasification with CCS) emerges as optimal for holistic sustainability, balancing low LCOH potential with carbon negativity, aligning with circular economy imperatives by valorizing waste and closing carbon loops. This supports net-zero transitions, particularly in waste-abundant regions, and underscores the role of incentives like 45Q credits in deployment.

Limitations include assumptions of U.S.-centric grid mix and feedstock prices, potentially varying globally, and exclusion of downstream H₂ use. Future research should explore higher PE ratios, advanced CCS solvents, or hybrid renewables to minimize penalties and scale-up viability, advancing bioenergy with CCS for climate goals.