Ariadna Yaneli Reséndiz-Jaramillo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ariadna Yaneli Reséndiz-Jaramillo has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Juvenal Rodríguez-Reséndiz
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
3 shared publications - Luis Angel Iturralde Carrera
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Queretaro 76010, Mexico
3 shared publications - Yoisdel Castillo Alvarez
Environmental Engineering Career, Engineering Faculty, Universidad Peruana de Ciencias Aplicadas (UPC), Lima 15023, Peru
3 shared publications - Carlos D. Constantino-Robles
Faculty of Chemistry, Autonomous University of Querétaro, Querétaro 76010, Mexico
2 shared publications - Carlos Fuentes-Silva
Engineering Division, Technological University of Corregidora, Corregidora 76924, Mexico
1 shared publication - Daniel Fernández Navarro
Facultad de Ingeniería Mecánica y Eléctrica (FIME), Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza 66451, Mexico
1 shared publication
Latest Publications
- Open Access
Artificial Intelligence-Assisted Photocatalysis: A Systematic Review of Materials Design, Performance Prediction, and Reactor Optimization with Emphasis on Plasmonic Systems
Plasmonic photocatalysis offers promising alternatives for solar energy conversion, hydrogen production, CO2 reduction, and pollutant degradation. However, its performance depends on multiple variables related to composition, structure, optical and electronic properties, charge transfer, operating conditions, and reactor configuration. This systematic review analyzes the application of artificial intelligence (AI) and machine learning in photocatalysis, with an emphasis on plasmonic materials, during the period 2020–2026. Using a PRISMA-based methodology, supplemented by bibliometric and technical–comparative analyses, we examined material design and selection, the prediction of photocatalytic performance, reactor optimization, and the construction of datasets. The results show that AI models enable the exploration of compositional and synthetic spaces, the prediction of optical and electronic properties, the optimization of heterojunctions and core–shell structures, and the estimation of H2 production, CO2 conversion, and pollutant degradation, in addition to reducing the computational cost of simulations through surrogate models. Integration with DFT calculations, CFD simulations, active learning, and inverse design expands its application to interconnected predictive and experimental systems. However, limitations persist regarding small and heterogeneous datasets, insufficient external validation, interpretability, and low transferability. Advances in the field will require standardized and traceable data, physically meaningful descriptors, and models that bridge the material, reaction, and reactor scales.
Open AccessArtificial Intelligence-Assisted Photocatalysis: A Systematic Review of Materials Design, Performance Prediction, and Reactor Optimization with Emphasis on Plasmonic Systems
Plasmonic photocatalysis offers promising alternatives for solar energy conversion, hydrogen production, CO2 reduction, and pollutant degradation. However, its performance depends on multiple variables related to composition, structure, optical and electronic properties, charge transfer, operating conditions, and reactor configuration. This systematic review analyzes the application of artificial intelligence (AI) and machine learning in photocatalysis, with an emphasis on plasmonic materials, during the period 2020–2026. Using a PRISMA-based methodology, supplemented by bibliometric and technical–comparative analyses, we examined material design and selection, the prediction of photocatalytic performance, reactor optimization, and the construction of datasets. The results show that AI models enable the exploration of compositional and synthetic spaces, the prediction of optical and electronic properties, the optimization of heterojunctions and core–shell structures, and the estimation of H2 production, CO2 conversion, and pollutant degradation, in addition to reducing the computational cost of simulations through surrogate models. Integration with DFT calculations, CFD simulations, active learning, and inverse design expands its application to interconnected predictive and experimental systems. However, limitations persist regarding small and heterogeneous datasets, insufficient external validation, interpretability, and low transferability. Advances in the field will require standardized and traceable data, physically meaningful descriptors, and models that bridge the material, reaction, and reactor scales.
- Open Access
A Multistage Sufficiency Test for Selecting Energy Performance Indicators in Industry: Beyond R2 Toward the Variable Associated with Significant Energy Use
Under ISO 50001, energy performance is monitored through Energy Performance Indicators (EnPIs) and energy baselines. In practice, the energy-to-production ratio (kWh/t) is commonly adopted by default and validated solely by the coefficient of determination (), which is insensitive to systematic bias, to the base load contained in the intercept, and to the residual structure that reveals an omitted explanatory variable. This work organizes well-established statistical and engineering checks into a sequential, four-outcome decision procedure anchored to the diagnosis of Significant Energy Uses (SEUs): retain the simple ratio, adopt a regression baseline with the same variable, switch to the SEU-associated variable, or reject the model as structurally misspecified. Relative to common practice, the procedure makes three methodological corrections explicit: in-sample NMBE is identically zero for OLS models with an intercept and is therefore defined out of sample; residual diagnostics are evaluated against exact, design-specific Durbin–Watson critical values with a Šidák-corrected family-wise error of 0.044–0.050 (versus ≈0.14 uncorrected); and the candidate-variable step uses a partial F-test on nested models, since the naive residual-versus-variable regression is attenuated by collinearity with production. The procedure is characterized on synthetic data with known truth ( replicates per cell): against an interannual drift of ≈2%/yr, its sensitivity reaches 1.00 at months while an -only criterion has sensitivity 0.00, and with a base-load fraction of ≈0.28 the -only rule retains the biased ratio in 100% of the replicates; specificity under a correct ratio is 0.95–0.96, and the adopted thresholds lie in a stable region of the (, ) sensitivity sweep. The procedure is then demonstrated on six industrial cases; most notably, in a fuel oil power plant a pooled baseline with is rejected (Durbin–Watson versus an exact critical value of 1.64; runs test ) because of drift in specific fuel consumption that cannot detect, and its out-of-sample validation over 37 rolling origins shows that an aggregated bias of can mask an origin-to-origin drift from to . The contribution is not a new indicator or a new statistic, but the integration of indicator selection and multistage statistical validation into a single auditable decision procedure whose operating characteristics are quantified.Open AccessA Multistage Sufficiency Test for Selecting Energy Performance Indicators in Industry: Beyond R2 Toward the Variable Associated with Significant Energy Use
Under ISO 50001, energy performance is monitored through Energy Performance Indicators (EnPIs) and energy baselines. In practice, the energy-to-production ratio (kWh/t) is commonly adopted by default and validated solely by the coefficient of determination (), which is insensitive to systematic bias, to the base load contained in the intercept, and to the residual structure that reveals an omitted explanatory variable. This work organizes well-established statistical and engineering checks into a sequential, four-outcome decision procedure anchored to the diagnosis of Significant Energy Uses (SEUs): retain the simple ratio, adopt a regression baseline with the same variable, switch to the SEU-associated variable, or reject the model as structurally misspecified. Relative to common practice, the procedure makes three methodological corrections explicit: in-sample NMBE is identically zero for OLS models with an intercept and is therefore defined out of sample; residual diagnostics are evaluated against exact, design-specific Durbin–Watson critical values with a Šidák-corrected family-wise error of 0.044–0.050 (versus ≈0.14 uncorrected); and the candidate-variable step uses a partial F-test on nested models, since the naive residual-versus-variable regression is attenuated by collinearity with production. The procedure is characterized on synthetic data with known truth ( replicates per cell): against an interannual drift of ≈2%/yr, its sensitivity reaches 1.00 at months while an -only criterion has sensitivity 0.00, and with a base-load fraction of ≈0.28 the -only rule retains the biased ratio in 100% of the replicates; specificity under a correct ratio is 0.95–0.96, and the adopted thresholds lie in a stable region of the (, ) sensitivity sweep. The procedure is then demonstrated on six industrial cases; most notably, in a fuel oil power plant a pooled baseline with is rejected (Durbin–Watson versus an exact critical value of 1.64; runs test ) because of drift in specific fuel consumption that cannot detect, and its out-of-sample validation over 37 rolling origins shows that an aggregated bias of can mask an origin-to-origin drift from to . The contribution is not a new indicator or a new statistic, but the integration of indicator selection and multistage statistical validation into a single auditable decision procedure whose operating characteristics are quantified. - Open Access
- 9 June 2026
- Published by MDPI AG in Clean Technologies
- Vol. 8 (3) , 88
Quantitative Indicators of the Circular Economy for Covered Pond-Type Bioreactors in Tropical Regions: Application to a Large-Scale Pig Farming System
Anaerobic digestion is a viable pathway to mitigate environmental impacts from swine manure in tropical regions while contributing to circular economy strategies. However, no standardized or integrated framework currently exists that simultaneously quantifies the closure of energy, material, carbon, nutrient, and water loops at the farm scale. This research presents the techno-economic design and environmental assessment of a covered, mechanically agitated lagoon biodigester for a 10,000-head swine fattening module located in Matanzas, Cuba. The system is sized by integrating hydraulic, thermal, and structural parameters, and its economic viability is assessed through Net Present Value (NPV = $1.09 million), Internal Rate of Return (IRR = 32%), and a payback period of approximately three years. A comparative screening-level life cycle assessment shows that biogas-based electricity generation substantially reduces impacts on climate change, air quality, and fossil fuel scarcity compared with conventional diesel-based generation, with trade-offs in eutrophication and ecotoxicity. As a key methodological contribution, five quantitative circular economy indicators are proposed and calculated: the Energy Self-Sufficiency Ratio (ESSR = 1.71), the Waste Valorization Index (WVI = 0.91), the Decarbonization Index (DCI = 6.7), the Fertilizer Substitution Rate (FSR = 16.3 t N year−1), and the Water Closure Factor (WCF = 1.30). These indicators show that the system achieves a 71% net energy surplus, valorizes over 90% of the input mass, avoids 6.7 times more emissions than it generates, replaces synthetic fertilizers, and returns more water than it consumes. The findings provide quantitative evidence that the convergence of mesophilic operation without auxiliary heating, high carbon intensity of the power grid, and availability of agricultural land enhances circularity performance in tropical covered lagoon bioreactors, and the proposed integrated indicator framework, aligned with ISO 59020:2024, provides a reproducible and transferable methodological basis for the comparative assessment of anaerobic digestion systems for livestock waste.
- 9 June 2026
- Published by MDPI AG in Clean Technologies
- Vol. 8 (3) , 88
Open AccessQuantitative Indicators of the Circular Economy for Covered Pond-Type Bioreactors in Tropical Regions: Application to a Large-Scale Pig Farming System
Anaerobic digestion is a viable pathway to mitigate environmental impacts from swine manure in tropical regions while contributing to circular economy strategies. However, no standardized or integrated framework currently exists that simultaneously quantifies the closure of energy, material, carbon, nutrient, and water loops at the farm scale. This research presents the techno-economic design and environmental assessment of a covered, mechanically agitated lagoon biodigester for a 10,000-head swine fattening module located in Matanzas, Cuba. The system is sized by integrating hydraulic, thermal, and structural parameters, and its economic viability is assessed through Net Present Value (NPV = $1.09 million), Internal Rate of Return (IRR = 32%), and a payback period of approximately three years. A comparative screening-level life cycle assessment shows that biogas-based electricity generation substantially reduces impacts on climate change, air quality, and fossil fuel scarcity compared with conventional diesel-based generation, with trade-offs in eutrophication and ecotoxicity. As a key methodological contribution, five quantitative circular economy indicators are proposed and calculated: the Energy Self-Sufficiency Ratio (ESSR = 1.71), the Waste Valorization Index (WVI = 0.91), the Decarbonization Index (DCI = 6.7), the Fertilizer Substitution Rate (FSR = 16.3 t N year−1), and the Water Closure Factor (WCF = 1.30). These indicators show that the system achieves a 71% net energy surplus, valorizes over 90% of the input mass, avoids 6.7 times more emissions than it generates, replaces synthetic fertilizers, and returns more water than it consumes. The findings provide quantitative evidence that the convergence of mesophilic operation without auxiliary heating, high carbon intensity of the power grid, and availability of agricultural land enhances circularity performance in tropical covered lagoon bioreactors, and the proposed integrated indicator framework, aligned with ISO 59020:2024, provides a reproducible and transferable methodological basis for the comparative assessment of anaerobic digestion systems for livestock waste.