Engineering

Abstract: This paper develops a reproducible spectral-thermodynamic model for ideal single-junction photovoltaic conversion under the ASTM G173 AM1.5G terrestrial spectrum and the ASTM E490 air-mass-zero (AM0) extraterrestrial spectrum. The formulation converts tabulated spectral irradiance to photon flux, evaluates step-function absorption, and enforces radiative detailed balance through the cell blackbody emission current. A bounded voltage-retention factor, 0 < Vf ≤ 1, is introduced as a phenomenological sensitivity parameter for aggregate voltage losses without permitting energy creation. Spectral Shannon entropy is retained only as a grid-controlled distribution descriptor; it is not subtracted directly from an energy flux. At 300 K and with one-sided radiative emission, the calculated AM1.5G maximum is 33.71% near a 1.34-eV bandgap, while the E490 AM0 model window yields 30.60% near 1.26 eV and a larger absolute power density. For AM1.5G, reducing Vf from 1.00 to 0.85 lowers the optimum efficiency from 33.71% to 28.18%. A silicon temperature study using a Varshni bandgap relation predicts decreasing open-circuit voltage and radiative-limit efficiency from −100 °C to 100 °C. The resulting model unifies terrestrial, space, voltage-loss, spectral-distribution, and temperature analyses while clearly separating theoretical limits from realizable device performance.

Abstract: The Lokbatan field, located in the Absheron Peninsula, represents an important example of the spatial and genetic association between hydrocarbon reservoirs and active mud volcanism. This study investigates the long-term evolution of Horizon VIII of the Lokbatan field and examines the relationship between reservoir development, production dynamics, and mud volcano activity. Historical production and reservoir data covering 1934–2024 were integrated with documented eruptive events. The dynamics of oil, water, liquid, and gas production, gas factor, and water cut were evaluated using Shewhart statistical control charts to identify long-term shifts in the production regime and deviations from established control ranges. The results reveal a pronounced transition from an early stage of intensive hydrocarbon production to a mature stage characterized by strongly depleted production conditions. Oil production reached exceptionally high values during the initial development period but subsequently declined and remained close to or below the lower control limit during the later decades. Liquid and water production also showed a long-term reduction, whereas water cut evolved toward a persistently high, water-dominated production regime, reaching a maximum of 94.9% in 1993 and remaining above 80% during most of the recent development period. Gas production and gas factor exhibited an overall decline, although the gas factor showed an exceptional increase during 1995–1997, reaching 871 m3/t in 1996. The analysis further indicates that intensive hydrocarbon extraction was associated with changes in the frequency and character of mud volcanic eruptions, whereas available monitoring data show no measurable short-term effect of individual eruptions on reservoir pressure or well productivity. The novelty of this study lies in integrating long-term reservoir performance indicators with Shewhart statistical process control and historical mud-volcano activity within a single analytical framework. This approach makes it possible to identify major transitions in the development regime of a mature hydrocarbon field while assessing its interaction with an active mud-volcanic system. The Lokbatan field thus provides a valuable case study of long-term reservoir depletion, increasing water dominance, changing gas behavior, and reservoir–mud volcano interaction in a tectonically active petroleum system.

Abstract: The major challenge in photovoltaic (PV) maximum power point tracking (MPPT) systems is finding a balance between the high performance of artificial intelligence techniques and the interpretability and reliability of physics-based approaches. This paper proposes a new MPPT controller based on a combination of Lagrangian and artificial intelligence techniques. In the proposed method, power maximization is modeled as a Lagrangian system, and the duty cycle is determined by physics equations. An artificial neural network is utilized to adaptively adjust the parameters of inertia and damping in real-time based on an eight-dimensional feature vector. Simulation results for step changes, ramp changes, and partial shading conditions confirm the effectiveness of the approach. The controller has 99.7% tracking efficiency in 18.2 ms, which is superior to P&O (55 ms), INC (45 ms), PSO (28.3 ms), and conventional ANN (22.5 ms). Under partial shading conditions, the controller correctly identifies the global maximum power point. The steady-state ripple is very low (±0.1 W), and the transient energy losses are significantly reduced compared to the benchmark algorithms. The results confirm that the integration of Lagrangian dynamics with adaptive neural tuning provides a systematic and efficient approach for designing reliable PV energy systems, effectively bridging the gap between data-driven and physics-based methods.

Abstract: This study investigates the integration of a bio-sourced building material-specifically, conventional clay enhanced with 10% eggshell powder-into the envelope of a typical two-level building in Morocco. The thermophysical characterization of this composite (thermal conductivity, specific heat capacity, and density) was implemented into EnergyPlus to evaluate its application at the whole-building scale through dynamic thermal simulations. The research quantifies the composite's impact on indoor temperatures, energy consumption, and thermal comfort across three representative Mo-roccan climates: Casablanca (Nouacer), Tangier, and Nador. The simulation results demonstrate that integrating the 10% eggshell composite into the building envelope reduces heating and cooling energy demands by up to 40% compared to conventional clay. Furthermore, the material significantly improves indoor thermal comfort by re-ducing total annual discomfort hours outside the thermal comfort setpoint range (20.00-24.00 °C) by up to 40%, offering a viable pathway for greening the building stock through agricultural waste valorization.

Abstract: Bullheading is a well control technique where heavy kill fluid is pumped down a wellbore to force a gas influx back into the formation, but poor estimation of the required injection pressure has historically led to failed interventions, most notably during the Macondo/Deepwater Horizon blowout. This study numerically simulates bullheading operations in a deepwater reservoir setting using the Petrel E&P platform, focusing on bottomhole pressure (BHP) behavior and the injection pressure needed to displace an influx. A vertical-well grid model representative of deepwater turbidite reservoirs was built, and a sensitivity analysis was performed across five reservoir/operational parameters, namely horizontal permeability, vertical permeability, porosity, net-to-gross (NTG) ratio, rock compressibility, and injection rate, using thirty-nine simulation cases. Horizontal permeability and NTG ratio were found to have the greatest influence on injection pressure, followed by porosity and rock compressibility, while vertical permeability showed no measurable effect. Injection rate, the only field-controllable variable, showed a strong direct relationship with both BHP and injection pressure. A subsequent uncertainty and cluster analysis using horizontal permeability and NTG ratio identified representative low, mid, and high pressure scenarios. These results indicate that reservoir characterization, particularly of horizontal permeability and NTG is critical to accurately predicting injection pressure for safe bullheading design in deepwater wells, and that simulation outputs should be validated against downhole pressure gauge data to reduce estimation uncertainty.

Abstract: Geothermal resource quality alone does not determine whether a project can be developed under favorable local and institutional conditions. This study integrates thermodynamic resource quality with site-level development feasibility for 18 Japanese geothermal sites included in a published Specific Exergy Index (SExI) dataset. Development feasibility is represented by a weighted score based on four criteria: hot-spring conflict risk, environmental and zoning constraints, consensus-building and regional acceptance, and infrastructure accessibility. SExI and development feasibility show a positive but statistically non-significant association (Pearson r = 0.430, p = 0.075). Using thresholds of 0.5 on both axes, 5 sites are classified as high SExI/high feasibility, 1 as high SExI/low feasibility, 6 as low SExI/high feasibility, and 6 as low SExI/low feasibility. Alternative weighting scenarios preserve the baseline quadrant membership, although sites near the feasibility threshold remain more sensitive to scoring assumptions. These results show that thermodynamic quality and development feasibility are related but distinct dimensions. The proposed matrix provides an exploratory screening framework for identifying whether geothermal development is primarily limited by resource quality or by site-specific socio-institutional conditions.

Abstract: The transition towards near zero-energy buildings requires advanced envelope solutions to mitigate thermal transmission losses and microclimatic discomfort inherent to highly glazed façades. Conventional passive triple glazing frequently induces a ‘cold-pane’ effect during extreme winter conditions, causing radiant asymmetry and buoyancy-driven convective downdraughts. This study empirically evaluates an active triple-glazed unit integrated with a transparent resistive heating layer. Investigations within a dual-zone climatic chamber simulated severe sub-zero boundary conditions down to −25 °C. Conjugate heat transfer was quantified using high-resolution thermography and heat-flux sensor arrays, whilst local discomfort was assessed applying Fanger’s criteria (ISO 7730). The findings reveal that passive operation at −25 °C depresses inner surface temperatures to 12.4 °C, yielding heat losses up to −65.0 W/m² and severe perimeter dissatisfaction (predicted percentage of dissatisfied, PPD > 20.4%). Under such conditions, architectural window-to-wall ratios (WWR) must be restricted to 24.6%. Conversely, activating the heating setpoint to 40 °C establishes a near-adiabatic thermal barrier (mean flux −8.2 W/m²), effectively suppressing downdraughts and ensuring microclimatic stability (PPD < 10%). Consequently, dynamic thermal modulation via active glazing significantly relaxes traditional architectural constraints; while passive configurations necessitate a restrictive WWR limit of approximately 25% under sub-zero conditions, the active system demonstrates the feasibility of an unconstrained 100% WWR, provided that the associated electrical energy demand is managed through intelligent control strategies to ensure holistic nZEB compliance.

Abstract: In this study, a new pressure stabilization (PS) system composed of a gas stable vessel (SV), PID control system and a reaction vessel (RV), etc. was used to study the dynamic characteristics of methane separation from low concentration coalbed methane com-pared with PV system. A porous medium system was constructed by sodium lignosul-fonate (SL) or calcium lignosulfonate (CL) solution which could will reduce gas-liquid interfacial tension, and enhance methane dissolution and mass transfer. At the same time, a thermodynamic accelerator of cyclopentane (CP), which effectively reduced hydration reaction conditions and improved methane storage rate, was added to the hydration reaction solution. The experiments were carried at 275.15 K and 3.0 MPa, the mass concentration of lignin was 500 ppm, the volume ratio of CP to deionized water was 1:10. The results shown that higher CH4 recovery (66.7%) and higher gas uptake (0.1139 mol) were obtained in PS + SL + CP system, but shorter t90 (154 min) and higher CH4 concentration (69.5%) in the hydrate phase were appeared under PV + SL + CP system, however shorter induction time (9 min) was found in PS + CL + CP system.

Abstract: The Chang 9 oil-bearing interval in the Beiliang area of Wuqi is an important oil-bearing interval in the lower assemblage of the Yanchang Formation in the Ordos Basin. To clarify its reservoir characteristics and the main controls on hydrocarbon accumulation, this study integrates stratigraphic correlation of 163 wells, sedimentary-facies analysis of 10 representative wells, log-derived porosity and permeability data, thin-section and scanning-electron-microscope observations, and pressure–temperature and fluid data. The results show that the K₀ marker bed at the top of the Chang 9 interval is 1.1–7.9 m thick (2.5 m on average) and that the interval can be subdivided into three sub-members: Chang 9₁, Chang 9₂, and Chang 9₃. The predominant sedimentary facies are delta-front subfacies, and the subaqueous distributary-channel sand bodies constitute the principal reservoir rocks. The reservoir lithologies are dominated by fine-grained feldspathic sandstone and lithic feldspathic sandstone, and the storage spaces are mainly residual intergranular pores and dissolution pores. The average porosities of the Chang 9₃, Chang 9₂, and Chang 9₁ sub-members are 9.66%, 8.23%, and 8.32%, respectively, and the corresponding average permeabilities are 5.64 × 10⁻³, 4.63 × 10⁻³, and 4.28 × 10⁻³ μm². The permeability variation coefficient ranges from 0.71 to 2.07, indicating low-porosity, low-permeability, and strongly heterogeneous reservoirs. The Chang 9 reservoirs are predominantly lithologic, with local modification by low-amplitude structures. The average initial formation pressure is 17.26 MPa, the pressure coefficient is 0.78, and the average formation temperature is 70.25 °C, defining a normal-temperature, low-pressure system. The formation-water salinity ranges from 5.19 to 10.59 g/L (7.53 g/L on average), and the water is mainly of the CaCl₂ type. Regional oil–source correlation indicates that the Chang 7 hydrocarbon source rocks are the principal hydrocarbon-supply interval, whereas the dark shales at the top of the Chang 9 interval may provide a supplementary contribution whose magnitude has not yet been quantified. Hydrocarbon enrichment is mainly controlled by the hydrocarbon-supply conditions, the distribution of subaqueous distributary-channel sand bodies, and reservoir effectiveness and connectivity; low-amplitude nose-shaped uplifts locally modify the oil–water distribution. Regional excess-pressure differences may have provided a driving background for downward migration, but this interpretation still requires verification through paleopressure reconstruction.

Abstract: Comparing heterogeneous energy pathways through a single hydrogen output can be misleading when model scale, conversion physics and evidential quality differ between pathways. This study presents a traceable numerical screening framework for photovoltaic (PV), concentrated solar power (CSP), geothermal energy and waste heat recovery (WHR) using nine PV scenarios, nine CSP scenarios, six geothermal scenarios and six WHR scenarios generated with ANSYS-based thermal or thermal-fluid models and MATLAB/Python post-processing. The PV branch provides documented DC power estimates of 33.31–65.44 W, with a mean of 51.69 W. The thermal branches are analyzed separately: their turbine workbooks contain static pressure differences and a fixed volumetric flow calculation of 1.8044 cubic metres per second for every scenario, but no documented mass flow or enthalpy coupling to the upstream heat exchanger models, rotor torque or complete expansion states. The product of static pressure difference and volumetric flow is therefore retained only as a pressure-flow power-scale diagnostic, and the subsequent efficiency scaling is reported as an illustrative electrical equivalent rather than turbine output. Within the WHR diagnostic set, excluding the deliberate high-flow stress case reduces the mean electrical equivalent from 6.635 to 4.245 kW, showing a 56.3% sensitivity of the arithmetic mean to that single operating point. Hydrogen estimates for PV and hydrogen equivalents for the thermal diagnostics are reported in separate evidential classes and are not compared as technology performance. The numerical dataset does not demonstrate common capacity/resource normalization, grid independence, quantitative validation or a closed energy balance from thermal resource to turbine. The contribution is therefore methodological: a transparent screening architecture that preserves the physical meaning, scale, coupling status and evidence level of each quantity before any downstream interpretation.

Abstract: This work presents a model predictive control (MPC) approach for optimizing the powertrain system efficiency of a fuel cell electric heavy-duty vehicle. The MPC determines the power split between the high-voltage traction battery and the fuel cell system so that the power demand of the drive cycle is met, while a second objective extends the lifetime of the fuel cell stack by including stack degradation in the cost function. Based on driving profile information, the controller adjusts the fuel cell power trajectory within a specified prediction horizon such that a target state of charge of the traction battery is reached at the end of the time-discrete horizon, fuel consumption is minimized and excessive degradation is avoided. The control variables are computed from discrete-time models of the fuel cell, the truck and the battery. The resulting non-linear optimization problem is solved with the open-source software package acados, integrated into MATLAB Simulink to achieve real-time capability. The MPC is implemented and tested in a model-in-the-loop environment. On the VECTO Long Haul cycle, hydrogen consumption is reduced by 6.5%, while membrane thinning and the loss of electrochemically active surface area are reduced by 6.1% and 2.3%, respectively, compared with a rule-based strategy.

Abstract: Geological CO₂ storage must operate within pressure and stress limits that preserve caprock, fault, and well integrity while sustaining climate relevant injection. Existing reviews often treat multiphysics simulation, machine learning, and physics informed learning separately, which obscures the different evidence required for stability screening, first slip, aseismic deformation, dynamic rupture, monitoring analytics, and containment consequences. This structured critical review integrates direct CO₂ storage observations, laboratory studies, injection analogues, multiphysics numerical methods, data driven machine learning, and scientific machine learning within a target specific evidence framework. We compare continuum, discontinuum, interface, and diffuse fracture formulations; one way, staggered, and monolithic coupling; field and laboratory validation; seismic, deformation, pressure, and fiber optic monitoring; and physics informed neural networks, neural operators, and reduced order models. The synthesis shows that pressure and deformation modeling and seismic signal processing are comparatively mature, whereas prospective fault slip and seismicity forecasting remain limited by uncertain in situ stress, fault connectivity, CO₂ conditioned friction, monitoring detection limits, model discrepancy, and scarce cross site validation. We propose task appropriate metrics, an explicit validation ladder, and a staged, human supervised digital twin roadmap. Machine learning and physics informed methods are most credible as bounded complements to verified simulators and monitoring systems, and operational readiness should be judged by uncertainty calibrated prospective evidence rather than algorithm novelty.

Abstract: Geological CO₂ storage is essential to pathways to carbon neutrality, but its deployment depends on trustworthy estimates of storage capacity, injectivity, trapping, reactive evolution, and containment. Digital rock physics can provide these estimates from X-ray and electron microscopy images, yet every result depends on image segmentation, which converts grayscale data into pore, mineral, fracture, and fluid phases. This review evaluates classical methods, machine learning, deep learning, transformers, and foundation models according to whether they support reliable storage decisions rather than image overlap scores alone. Evidence is synthesized from imaging of dry rocks, CO₂–brine experiments, multiscale studies of carbonates and shales, and analyses of fractured rocks. We introduce a framework with seven dimensions: class accuracy, boundary fidelity, topology, morphology, calibrated uncertainty, sensitivity of physical properties, and consequences for engineering decisions. The evidence shows that visually similar segmentations can yield substantially different predictions of permeability, connected porosity, residual trapping, reactive surface area, and leakage paths when errors occur at critical pore throats, fluid interfaces, or fractures. We therefore recommend selecting methods according to storage task and lithology, validating them on independent samples, propagating ensembles of plausible segmentations, using metrics that account for topology, and comparing predictions with laboratory measurements. The central message is simple: segmentation should be treated as both a measurement process and a form of risk control. Segmentation with auditable and quantified uncertainty can reduce false site acceptance or rejection, improve injection and monitoring design, and strengthen geological CO₂ storage as a technology for carbon neutrality.

Abstract: This study evaluates the integrated sustainability potential of WtE technology scenarios across eight South African metropolitan municipalities, which collectively represent 40.14% of the national population. The assessment considers energy generation, greenhouse gas (GHG) emission reduction, landfill space savings, waste diversion, financial feasibility, and technology readiness, in alignment with the Waste-to-Energy Roadmap, National Waste Management Strategy, and Integrated Resource Plan. Seven scenarios were evaluated for the period 2028–2050, incorporating recovery rates that increase at 7–8-year intervals to ensure sustainable feedstock supply. These include S1 (Business-as-Usual/Landfilling), S2 (Landfilling with upgraded landfill gas recovery, LFG+), S3 (LFG+ with anaerobic digestion (AD)), S4 (LFG+ with AD and incineration), S5 (LFG+ with AD and pyrolysis), S6 (LFG+ with AD and gasification), and S7 (LFG+ with AD and plasma gasification). An Integrated Sustainability Performance Index (ISPI), combining 12 normalized energy, environmental, financial, and technological indicators, identified S4 as the most sustainable scenario, with an average score of 0.688 ± 0.044 and first-place ranking in all municipalities. S3 ranked second at 0.520 ± 0.085, while S7 ranked last. S2–S3 remained financially attractive, whereas advanced thermal technologies benefited municipalities with larger waste volumes. Integrated WtE systems support South Africa’s energy transition and sustainable waste management.

Abstract: The evolving hydrogen transmission network is a key component of Europe’s energy transition. This study assesses whether the planned European hydrogen transmission grid can accommodate projected regional supply and demand in 2030, 2040, and 2050. Scenario data from the TransHyDE System Analysis project are combined with GIS-based network models and European-scale hydraulic simulations. The modeled topology integrates the European Hydrogen Backbone and the German hydrogen core network. The results indicate that, in 2030, the still-fragmented network can satisfy peak regional demand. By 2040, the grid develops into a largely meshed system, although elevated pressure levels occur in Spain and Italy because of regional supply–demand imbalances and high hydrogen imports from North Africa. The southern European pipelines and the interconnections between the Iberian Peninsula and Central Europe therefore appear insufficiently dimensioned under the investigated scenario. These effects become more pronounced in 2050 as hydrogen demand and imports increase. The findings indicate that additional transport capacity and strengthened cross-border interconnections may be required to ensure the reliable operation of the future European hydrogen network.

Abstract: The decarbonisation of the energy sector requires efficient strategies to reduce fuel consumption and greenhouse gas emissions. Organic Rankine Cycles (ORCs) have emerged as a promising technology for waste heat recovery due to their flexibility and ability to operate with low- and medium-temperature heat sources. This work develops a general mathematical framework, grounded in the Helmholtz energy function, to characterise the limiting and optimal efficiency of ORCs equipped with an Internal Heat Exchanger (IHE) when operating with dry and isentropic working fluids. The framework is exemplified using the van der Waals equation of state and extended to real fluids through the PC-SAFT model. Results show that integrating an IHE significantly enhances efficiency for drier working fluids, which expand deeper into the superheated vapour region, enabling greater internal heat recovery. Efficiency gains diminish at condenser temperature extremes, defining operational boundaries where IHE integration is less effective. From a practical perspective, minimising the temperature difference at the IHE outlet (ΔT_min) is critical to maximise performance. The proposed framework provides theoretical insight and practical guidelines for fluid selection and operating strategies in ORC-based waste heat recovery systems.

Abstract: The Molten Salt Fast Reactor (MSFR) concept, integrated into the iMAGINE framework, offers a transformative approach to nuclear waste management and fuel cycle closure. However, industrial deployment requires small-scale demonstrators like DEMO (demonstration fusion power plant) to validate reactor physics and safety under realistic conditions. This study investigates the central challenge of sustaining criticality in a 50 MWth demonstrator over a 20-year lifespan, with the innovative challenge to operate without employing a traditional mechanical control system. Using the HELIOS code package, simulations reveal that a reference burner core with 19.9% enrichment experiences a significant criticality loss of over 2600 pcm during reactor lifetime. Several non-mechanical compensation strategies were evaluated against operational goals. Over-feeding fissile material can stabilize criticality but risks deviating from the salt’s ideal eutectic composition. Relying on negative thermal feedback requires temperature adjustments of approximately 130 K, which may be "too challenging" for a first-of-a-kind system due to increased corrosion risks and narrow safety margins. Furthermore, while online salt clean-up of noble metals is vital for chemical validation, its direct contribution to reactivity is marginal. A central novel insight is the identification of a "design dilemma": although increasing core size (reducing enrichment to 14%) sustains criticality through enhanced breeding, it increases fuel costs by 2.5 times and extends the time to target burnup from 20 to about 60 years. Since the primary mission of a demonstrator is to produce high-burnup fuel for analysis quickly, larger cores are counterproductive. results of this study shows that no single method is ideal; instead, either a hybrid approach combining several of the investigated approaches or a future disruptive innovation, such as moderator control for HTGR, is essential to balance reactor physics with practical experimental objectives.

Abstract: This paper presents a Model Predictive Control (MPC)-based energy management strategy for hybrid power systems combining a proton-exchange membrane fuel cell (PEMFC) with a lithium iron phosphate (LFP) battery storage unit for renewable energy applications. The proposed framework optimizes power allocation between the two sources while respecting operational constraints, including current limits, power balance requirements, and state-of-charge (SOC) bounds with soft constraints to prevent overcharging and deep discharging. Unlike conventional rule-based approaches, the MPC formulation employs a quadratic cost function with tunable weighting factors that enable flexible prioritization of either fuel cell conservation or battery lifetime extension. Accurate yet computationally efficient models are developed for both components: an equivalent circuit model for the LFP battery and a theoretical electrochemical model for the PEMFC. The performance of the proposed strategy is validated through comprehensive simulations under realistic renewable generation and load profiles. Five case studies are examined, each representing different operational scenarios characterized by varying initial SOC conditions and component prioritization weights. The results demonstrate that the MPC-based approach effectively manages power distribution, maintains SOC within safe operating ranges, and adapts to changing system conditions. Quantitative analysis shows that the tunable weighting strategy successfully limits high-current events, reducing stress on the battery and extending its operational lifetime. The proposed framework offers a scalable and flexible solution for improving the reliability and economic viability of hybrid energy storage in modern renewable grids.

Abstract: Indirect dry cooling system (IDCS) serves as critical cooling equipment for power plants, whose cooling performance is strongly affected by ambient meteorological con-ditions. Thus, accurate cooling performance prediction is essential for the safe and ef-ficient operation of power plants. In this paper, a half-tower numerical model of the typical IDCS in a 2×660MW power plant is established due to geometric symmetry, and a multi-scale heat conservation model between the condenser and air-cooled heat exchanger is developed. Grid independence verification and validation under typical operating conditions prove that the model possesses satisfactory engineering accuracy for subsequent variable-condition simulations. Numerical results reveal that ambient wind enhances heat transfer of windward cooling sectors while degrading that of lat-eral cooling sectors. Higher ambient temperature also weakens the system’s cooling capacity and tower ventilation performance. Based on the numerical results, multiple regression prediction models are established using ambient temperature and wind speed as independent variables. With these models, the overall system performance, inlet airflow rate, inlet air temperature and sector heat transfer characteristics are predicted rapidly and accurately. Most of the models have a coefficient of determina-tion (R2) over 0.99 and low root mean square errors, demonstrating high prediction precision. The proposed models effectively improve the computational efficiency for off-design conditions. It provides reliable theoretical and data support for operational optimization, performance prediction and structural modification of IDCS in power plants.

Abstract: This article discusses a study on the intelligent analysis of multisensory biogas data obtained from an experimental dataset generated by a Lab-on-Chip platform. The relevance of this work stems from the need for real-time monitoring of biogas quality and biomass condition under anaerobic digestion conditions, where changes in the concentrations of methane, carbon dioxide, hydrogen sulfide, oxygen, and temperature directly affect the stability of the technological process and the energy efficiency of the plant. This study utilizes a multisensor Lab-on-Chip/biosensor platform designed for rapid analysis of small samples of biogas, biomass, and biomix. The platform integrates gas, liquid, and optical sensor channels, as well as a module for transmitting data to the cloud. The experimental data obtained are processed using intelligent data analysis methods, including statistical analysis, correlation analysis, anomaly detection, and assessment of the relationships between monitored parameters. The scientific significance of this work lies in the application of an integrated approach to the analysis of multichannel experimental data obtained from the Lab-on-Chip platform, which enables a more accurate and timely assessment of the state of the biogas process. The practical significance lies in the ability to use the proposed approach for remote monitoring, early detection of anomalies, and improving the efficiency of biogas plant management.