Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Generalisable computer vision methods for endoscopic surveillance and surgical interventions(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-05) Ali, Mansoor; Ochoa Ruiz, Gilberto; emimmayorquin, emipsanchez; School of Engineering and Sciences; Campus Estado de México; Ali, SharibAmong the most prevalent cancers in humans are gastrointestinal (GI) cancers, which mostly include cancers originating from the esophagus, stomach, and colon. Endoscopy for the upper gastrointestinal (GI) tract and colonoscopy for the lower side are considered the gold standard techniques for screening and removing precancerous lesions and abnormal tissue growth like polyps with high sensitivity. Prior research has shown higher polyp miss rates due to their peculiar morphology, variability in shape or size, and appearance. Also, endoscopic surgical interventions offer a minimally invasive approach for lesion removal or for the treatment of other diseases inside the abdominal and reproductive organs. Despite being patient-friendly in reducing trauma, hospitalisation times, and quicker post-operative recovery, minimally invasive surgeries may become complicated due to increased cognitive burden and reduced field-of-view for the clinicians. Computer-assisted detection (CADe), diagnosis (CADx), and interventions (CAI) have shown promise in providing useful support to the clinicians in both disease diagnosis and treatment, with immense potential to further improvements as the data availability becomes easier due to the endoscopes. Deep learning is increasingly being leveraged to develop methods for improving the pre-cancerous lesion detection and diagnosis, reducing the missing rates and providing intraoperative assistance to surgeons for better decision-making. However, current methods suffer from the domain shift problem, i.e., they work well on the same distribution of data and perform poorly on out-of-the-distribution data, thus lacking the real-world deployment capability. This thesis explores the impact of domain shift in endoscopic domain data on the current state-of-the-art methods, investigates the research gaps, and proposes methods for improved disease detection, surveillance, and surgical interventions with better generalisation capability. Specifically, we aim to use the feature space of the encoder networks of the state-of-the-art segmentation methods to learn discriminant information for better domain-invariant learning and improving the model generalisation on unseen out-of-the-distribution endoscopic datasets. We propose various methods for polyp segmentation in upper and lower GI tract data, full scene segmentation in laparoscopic surgery, and depth estimation in abdominal surgery. We also introduce an annotated multicentre segmentation dataset for evaluating model performance on generalisability and encouraging further research. Our results indicate improved out-of-distribution performance on multi-domain and cross-center endoscopic data. We will further work on extending the data to enhance its size and variability and explore new methods to increase robustness and generalisation performance.
- Fabrication of binary and ternary semiconductors as gas sensing devices: stoichiometric design and functional engineering studies(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-05) Rueda Castellanos, Kevin; Karthik Tangirala, Venkata Krishna; mtyahinojosa, emipsanchez; Henao Martínez, José Antonio; Dutt, Ateet; García García, Andrés David; School of Engineering and Sciences; Campus Estado de México; García Farrera, BrendaMetal-oxide semiconductor (MOS) sensors play a key role in environmental monitoring, healthcare diagnostics, and industrial safety due to their robustness, scalability, and low fabrication cost. However, achieving reliable selectivity and stability under realistic conditions remains a major challenge, often limited by the interplay between material composition, defect chemistry, and synthesis-dependent microstructure. To address this issue, the present work investigates the Zn–Sn–O ternary system as a tunable materials platform for CO and acetone sensing, focusing on how synthesis route and stoichiometry influence structural and functional behavior. Three complementary fabrication methods were employed to produce Zn–Sn–O materials with controlled composition and morphology: physical vapor deposition by magnetron sputtering (PVD-RMS), ultrasonic spray pyrolysis (USP), and chemical co-precipitation (CP). Each method provided distinct thermodynamic and kinetic environments that governed phase formation, crystallinity, and grain morphology. The synthesized materials were systematically characterized through X-ray diffraction with Rietveld refinement, FTIR and Raman spectroscopy, XPS, and SEM/EDS to correlate synthesis conditions with crystal structure and surface features. Gas-sensing performance toward CO and acetone was evaluated using a custom-built dynamic sensing system under standardized temperature and concentration ranges, allowing direct comparison across thin-film and powder-based architectures. Among the tested samples, the SZ50-450-USP thin film exhibited the highest acetone sensing performance at 300 °C, with response and recovery times of 193 s and 207 s, respectively, and a maximum sensing response of 87 %. These results demonstrate that balanced Zn/Sn ratios and controlled microstructural evolution significantly enhance sensitivity and stability. Based on the structural and functional analyses, a sensing mechanism is proposed that links preferential crystallographic orientation, grain size, and oxygen-vacancy distribution to the adsorption–desorption dynamics of target gases. The comparative study highlights the importance of synthesis–structure–property relationships in optimizing gas-sensing performance and provides a reproducible framework for designing Zn–Sn–O-based semiconducting oxides for selective VOC detection, with potential applications in medical diagnostics via breath analysis.
- Human learning curve forecasting & optimization framework for manual assembly operations(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-04) Peña Olvera, Carlos Adrián; Romero Díaz, David Carlos; emipsanchez; Johansson, Björn; Ruiz Loza, Sergio; Escobar Castillejos, David Escobar; Departamento de Ingenieria y Ciencias; Campus Ciudad de México; Noguez Monroy, Juana JulietaThe manufacturing industry is undergoing a significant transformation, led by the widespread adoption of Industry 4.0 technologies and data-driven production management systems. While monitoring and optimization have become common for machine operations, manual operations are mostly disconnected from these advancements, due to persistent challenges in data acquisition and the intrusiveness of monitoring methods. More importantly, low-cost countries keep manual assembly a core part of their operations, based on costs and flexibility. This second element, however, presents a challenge to companies, due to human behavior not being as perfectly repetitive as machines, leading to differences between planned production time and actual production time. One factor not currently considered in planning cycle times and production capacity is the learning effect, represented by prolonged cycle times in the first production units, but improving over time. Traditional approaches to track the learning effect have seen little application on processes in recent times, resulting in missed opportunities for productivity forecasting and optimization. The primary objective of this thesis is to present a comprehensive framework for the collection, operational forecasting, and productivity enhancement of production cycle times in manual operations by leveraging data paired with a simple data collection method. This work proposes a novel human learning curve measurement and optimization solution that mirrors the sophistication of machine monitoring applied to humans. It also considers a data problem commonly found in the manufacturing industry, which is excessive data collection, making predictions and fitting curves computationally expensive, by considering a simplification method. Key contributions of this Ph.D. thesis include a state-of-the-art review on learning curves, learning curve parameterization methods, and data simplification techniques, which led to the development of a “Human Learning Curve Forecasting and Optimization Framework”. The Ph.D. thesis also presents both controlled and industrial experimentation for the validation of the framework. The Ph.D. thesis results present the benefits of analyzing the human learning effect in productivity, presenting the industry with the opportunity to take immediate action to improve and increase efficiency in the short- and long-term, ultimately integrating the human factor in the decision-making for performance improvements. The Ph.D. thesis presented calls for a change in the way manual operations are being analyzed, by considering the learning curve effect, analyzing it in the short- and long-term, and presenting an alternative way to plan production in line.
- A methodology to optimize water networks in buildings using digital technologies(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-04) Cortez Lara, Pedro Maximiliano; Barrios Piña, Héctor Alfonso; emimmayorquin, emipsanchez; Mora Polanco, Abrahan Rafael; Rangel Ramírez, José Guadalupe; Bustamante Bello, Martín Rogelio; Escuela de Ingeniería y Ciencias; Campus Puebla; Sánchez Andrade, BenjamínRecent advances in construction Digital Technologies (DT) have renewed interest in Building Information Modeling (BIM). At the same time, concerns about the environmental impact of the building sector continue to grow. Yet, studies that link BIM with Water Efficiency Analysis (WEA) remain limited. Most available work offers only simplified assumptions, partial simulations, or narrow approaches focused on specific modeling tasks. As a result, many buildings still rely on oversized Water Networks (WN). These systems increase carbon and water footprints and pose sanitary risks due to long periods of water stagnation. This research seeks to address these issues by developing a method to improve WN through DT while reducing their environmental impact. The study adopts a mixed approach that integrates BIM, Metaheuristics, and Input–Output (IO) analysis. The first part of the study analyzed the influence of Peak Water Demand (PWD) in WN and introduced a procedure to estimate it using standardized information. The method was evaluated through a residential case study. The results showed that the proposed approach provides consistent PWD estimates and performs better than the methods currently used in practice. The predicted demand was significantly lower, with values that were about 2.6 times smaller than those obtained through traditional procedures. The method also produced results that were close to the measurements collected on site. Even so, its purpose is not to replicate the exact observed values. A perfect match could reduce the safety margin and lead to undersized systems that fail during unusual or high-demand conditions. The second stage evaluated a methodology to integrate WEA within a BIM environment. Autodesk Revit was chosen as the primary platform because it is widely adopted and can connect different digital tools through a single model. The three proposed domains showed consistent improvements in water savings and reductions in electrical power. Their structure and customization increase the modularity of the methodology. This allows the process to adapt to projects of different scales while keeping a clear and practical workflow. These features help designers and professionals identify relevant elements and parameters early in the design phase. This leads to better water use outcomes and improves the performance of the WN throughout the following stages of the project. The third stage focused on creating a BIM-Metaheuristics algorithm to optimize WN. This part of the research stands out because it requires low technological resources while maintaining high precision in the selection of optimal pipe diameters. The method incorporates environmental factors and hydraulic constraints in a single optimization process. The results indicate that the model can reduce pipe sizes by one nominal diameter in most cases. In more demanding scenarios, the reduction can reach two diameters. These adjustments are obtained while decreasing environmental impact, lowering costs, and minimizing computational demands. The approach is flexible and can be applied to a wide range of building contexts. It consistently produces optimal configurations for WN. This contributes to a shift in how environmental performance is evaluated in plumbing design. This stage also explores the blue and carbon footprint assessment-based BIM-Input Output (BIM-IO) using Multifunctional Analysis of Regions Through Input-Output (MARIO) tool. The proposed framework demonstrated simplicity and ease of use for assessing Blue and Green water footprints in buildings using MARIO and BIM. BIM’s applicability across various building environments enables extensive data extraction. This includes detailed information from systems such as structure, architecture, HVAC, and plumbing. The level of detail depends on the Level of Detail (LOD) used in the model. The outcomes from the third stage were analyzed to determine the environmental impact and development of new policies.
- A comparative study on chemically and phytogenically synthesized TiO₂ nanoparticles and their role in modulating plant growth and metabolic dynamics(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-04) Bhatti, Atiya; Navarro López, Diego Eloyr; mtyahinojosa, emipsanchez; Sánchez Martínez, Araceli; Lozano Sánchez, Luis Marcelo; Mejía Méndez, Jorge L.; School of Engineering and Sciences; Campus Estado de México; López Mena, Edgar RenéThe present research provides a comprehensive investigation of the synthesis, characterization, and agricultural applications of titanium dioxide nanoparticles (TiO₂-NPs) developed via a conventional and eco-friendly (green synthesis method) route, focusing on their interactions with plant growth-promoting microorganisms (PGPMs) in order to boost the physiological and antioxidant performance of Capsicum annuum cultivars. The TiO₂-NPs synthesized through the molten salt method exhibited a nano-cuboid structure, a negative surface charge, and a moderate surface area. While green-synthesized TiO₂-NPs obtained from blueberry natural extracts using isopropanol (TiO₂-I.P) and methanol (TiO₂-M) exhibited mesoporous polyhedral anatase structures (E₉ ≈ 3.0 eV), hydrodynamic sizes of 130–150 nm, and stable ζ-potentials ranging from −33 to −50 mV. The extracts rich in flavonoid and phenolic compounds provided distinctive surface functionalities, improving the stability and bioactivity of the nanoparticles (NPs). In-vitro compatibility studies indicated that TiO₂-NPs facilitated microbial proliferation up to 150 µg/mL without exhibiting toxicity, thereby enhancing Bacillus thuringiensis (B.t) (1.56–2.92×10⁸ CFU/mL) and Trichoderma harzianum (Th) (2.50–3.90 × 10⁸ spores/mL), greenhouse experiments revealed significant enhancements in plants shoot and root growth, as well as increases in fresh weight (F.W) and dry weight (D.W) biomass and chlorophyll content. When TiO₂-NPs were utilized either independently or in combination with PGPMs B.t, Th. The synergistic treatments significantly improved antioxidant and enzymatic responses. Including peroxidase (69.90 UA/g F.W), β-1,3-glucanase (2.45 nkat/g FW), total phenolic content (29.50 GA/g FW), and chlorophyll accumulation (210.8 ± 11.4 mg/mg FW). In the context of green formulations were observed, TiO₂-I.P increased number of leaves and height of plant, whereas the root elongation not greater than control. Specifically with individual microorganism B.t, Th combined with at moderate concentration of TiO₂-I.P improved F.W and D.W. Peroxidase levels significantly increased when 50 µg/mL of TiO₂-M combined with microorganisms B.t+Th, While TiO₂-I.P stimulated a wider range of antioxidant responses, at 150 µg/mL of both formulation increased the total proteins. In case of total chlorophyll content increased at 150 µg/mL of TiO₂-I.P alone or combination of microorganism B.t+Th. The results emphasize biphasic behavior that is dependent on both formulation and dose, influenced by the surface chemistry of NPs and their compatibility with beneficial PGPMs. This work advances a practical framework for precision and sustainable agriculture through the integration of nanotechnology and microbial biotechnology. Where the surface chemistry of NPs and their biological compatibility can be tailored to ensure reliable and useful outcomes in agricultural fields.
- Design of novel oven-baked sweet potato snack enhanced with brewery byproduct: the effect on sensory and nutritional content(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-03) Gómez Cisneros, Analaura; Calderón Oliver, Mariel; emimmayorquin, emipsanchez; Escalante Alburto, Anayansi; Pino Espinosa Ramírez, Johanan del; Ponce Alquicira, Edith; School of Engineering and Sciences; Campus Monterrey; Santos Sea, LilianaThis doctoral thesis investigates the potential of converting sweet potatoes into flour and analyzes how different drying techniques affect the preservation of bioactive compounds. It also assesses the role of ultrasound as a pretreatment strategy to improve the content of these compounds during processing. The obtained flour was subsequently used on the design of a novel baked snack enriched with brewer's spent grain (BSG), a by-product of the brewing industry, to evaluate the effects of this enrichment on its sensory and nutritional attributes. The main motivation lies in the growing demand for more nutritious snacks and the valorization of agro-industrial by-products. The general objective was to apply and evaluate the effects of ultrasound, storage, and drying methods in the processing of sweet potato to obtain flour with increased phenolic and carotene content, and to use it as an ingredient in an oven-baked snack enriched with BSG, aiming to achieve improved nutritional properties and acceptable sensory characteristics. The methodology included the production of sweet potato flour (SPF), evaluation of the effect of ultrasound treatment, different storage times (0, 24, 48, 72, and 96 h), and drying methods (dehydration and freeze-drying) on the content of bioactive compounds (phenolics and carotenoids), and physicochemical properties. Subsequently, a base snack was formulated using the produced sweet potato flour with corn flour and wheat flour, and the proportions were optimized through mixture design, based on sensory evaluation, and texture analysis. Finally, different proportions of ground BSG (6.25%, 12.5%, 18.75%, and 25%) were added to the base snack to evaluate its impact on sensory properties, color, texture, nutritional composition (moisture, fat, protein, fiber, ash, starch), and content of bioactive compounds (phenolics, carotenoids, and antioxidant activity). The results showed that ultrasound treatment significantly affected the polyphenol content of sweet potato flour, increasing it by 93%, highlighting the effectiveness of US as an abiotic elicitor for the accumulation of phenolic compounds even in processed foods. The drying method influenced the carotenoid content, achieving 65% more content when the samples were freeze-dried. In addition, the b* and L* values were affected by these two factors, resulting in higher values for both parameters. The processing methodology was defined as the use of US-pretreatment, storage for 48 h and dehydration, and the produced flour was used to develop a novel oven-baked snack with nixtamalized corn (CF) and wheat flour (WH). The optimal formulation consisted of 28.29% SPF, 41.45% CF, and 30.3% WF, achieving satisfactory overall acceptability with a score of 7.05±0.22 ("moderate liking"). The addition of BSG (up to 25%) to the optimized formula significantly modified the color by reducing the a* and b* values, and texture by increasing the hardness, gumminess, and chewiness of the snack. However, the overall sensory acceptance according to the hedonic scale remained at acceptable levels with scores above 6, defined as like slightly, even with the addition of up to 25% BSG. Nutritional analysis showed that adding BSG significantly increased 1.6 more times of dietary fiber and double the ash content, as well as three times more in the phenolic content and 33% more in antioxidant activity of the snack. The formulation containing 6.25% BSG proved to be the best final formula, offering the greatest balance between improved nutritional properties and favorable sensory attributes, with the highest overall acceptability score. Overall, this work brings together two main contributions: first, it proposes a novel processing strategy for sweet potato that combines ultrasound, cold storage, and dehydration, which resulted in a flour with enhanced bioactive content. Rather than limiting the contribution to improved flour, the study extends this development into an oven-baked snack that aligns with consumer demand for more nutritious foods. The combination of the produced sweet potato flour with corn, wheat, and brewer’s spent grain represents a mixture that has been scarcely explored, and working with it provided new information on how they can improve the nutritional profile of an oven-baked snack. These findings not only offer a promising direction for new added-value snack products but also respond to current trends in health-focused eating and sustainable food development. By valorizing an agro-industrial by-product and incorporating plant-based ingredients, the final product contributes to more responsible and forward-looking food systems.
- Microalgae-based bioremediation of food and beverage processing wastewater: A sustainable approach toward a circular economy concept(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-03) Najar Almanzor, César Eduardo; Carrillo Nieves, Danay; mtyahinojosa, emipsanchez; Luzardo Ocampo, Iván Andres; Gutiérrez Uribe, Janet Alejandra; Chairez Oria, Jorge Isaac; Detrell, Gisela; Santaeufemia Sánchez, Sergio; Escuela de Ingeniería y Ciencias; Campus Guadalajara; García Cayuela, TomásFood and beverage production generates large volumes of nutrient-rich wastewaters that pose severe environmental challenges when discharged untreated. Effluents such as nejayote (from tortilla production), tequila vinasses (from tequila distillation), and cheese whey (from cheese production) contain high organic loads and extreme pH values that contribute to eutrophication and ecosystem disruption. Developing sustainable technologies that mitigate pollution while enabling resource recovery is therefore essential for advancing circular and cleaner production. This thesis evaluates microalgae-based bioremediation as an alternative for the treatment and valorization of these agro-industrial effluents. The work encompasses algae adaptation, process scale-up, biomass characterization, and environmental assessment. A UV-mutagenesis and gradual acclimatization strategy enabled Chlorella vulgaris, Haematococcus pluvialis, and Anabaena variabilis to grow in undiluted wastewater, achieving pollutant reductions of 87–99.9% in nejayote, 31–81% in vinasses, and 35–56% in whey. Although substantial, these results indicate that microalgae are best suited as components of a hybrid treatment systems rather than standalone technology. The technology’s scalability was validated through the cultivation of H. pluvialis in 100-L raceway pond, which maintained high remediation performance and biomass productivity despite minor declines associated with evaporation. The biomass showed significant protein and ash content, supporting potential use as biofertilizer, feed ingredient, or nutraceutical ingredient. Biochemical and functional characterization of biomass grown in nejayote and tequila vinasses revealed reduced pigment and phenolic content due to cultivation stress. However, extracts retained cytokine-modulating activity in RAW 264.7 macrophages, indicating potential for use as nutraceutical ingredient, animal feed, or biofertilizers following safety validation. Life Cycle Assessment comparing a microalgae-based vinasse treatment with the conventional industrial process showed similar overall environmental burdens but substantial reductions in terrestrial ecotoxicity and human carcinogenic toxicity. It also highlighted the need for optimization in coagulant sourcing and energy integration. Overall, this work demonstrates that microalgae-based treatment of agro-industrial effluents is technically viable, environmentally promising, and aligned with a circular bioeconomy, while identifying key challenges that must be addressed to enable industrial implementation.
- Effect of porosity and microstructure defects in out-of-plane properties of 3D printed composite materials made by continuous fiber reinforcement(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-03) Moreno Núñez, Benjamín Alberto; Treviño Quintanilla, Cecilia Daniela; mtyahinojosa, emipsanchez; Cuán Urquizo, Enrique; Sánchez Santana, Ulises; Pérez Santiago, Rogelio; School of Engineering and Sciences; Campus Monterrey; Pincheira Orellana, GonzaloThis research presents a comprehensive experimental and predictive analysis of 3D printed composite materials (3DPCM) made with Onyx reinforced Kevlar fibers. The mechanical behavior was characterized through three in-plane (tensile, compression and flexural) and four out-of-plane (Mode I, Mode II, Mixed-Mode I/II fracture, and short-beam strength) tests to evaluate both intralaminar and interlaminar responses. In-plane results revealed a strong dependence on fiber orientation, with the 0° fiber orientation achieving higher tensile and flexural, while the 90° fiber orientation exhibited slightly greater compressive modulus. Out-of-plane results demonstrated higher Mode I fracture toughness in 90° fiber orientation, whereas Mode II and mixed mode responses were dominated by shear effects, following the relationship 𝐺𝐼𝐼𝑐 >𝐺𝐼𝑐 > 𝐺𝐼/𝐼𝐼𝑐. Microstructural analysis identified voids, matrix peeling, fiber exposure, and poor impregnation as the key defects influencing crack initiation and delamination. Also, void content of the samples demonstrated an impact in mechanical properties as in traditionally made composites, the higher the void content the higher the mechanical variation. Finally, a machine-learning predictive model was developed to predict load-displacement curves in Short-Beam Strength tests, enabling accurate prediction of mechanical responses (𝑅2 > 0.94) based on number of fiber layers and fiber orientation configuration. These findings highlight the strong coupling between printing parameters and mechanical performance, providing valuable insights for the design and optimization of 3DPCM.
- A methodology to select downsized object detection algorithms for resource-constrained hardware using custom-trained datasets(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-03) Medina Rosales, Adán; Ponce Cruz, Pedro; emipsanchez; López Cadena, Edgar Omar; Montesinos Silva, Luis Arturo; Balderas Silva, David Christopher; Ponce Espinosa, Hiram Eredín; School of Engineering and Sciences; Campus Ciudad de MéxicoDownsized object detection algorithms have gained relevance with the exploration of edge computing and implementation of these algorithms in small mobile devices like drones or small robots. This has led to an exponential growth of the field with several new algorithms being presented every year. With no time to test them all most benchmark focus on testing the full sized versions and comparing training results. This however, creates a gap in the state of the art since no comparisons of downsized algorithms are being presented, specifically using custom built datasets to train the algorithms and restrained hardware devices to implement them. This work aims to provide the reader with a comprehensive understanding of several metrics obtained not only from training metrics, but also from implementation to have a more complete picture on the behavior of the downsized algorithms (mostly from the YOLO algorithm family), when trained with small datasets, by using a fiber extrusion device with three classes: one that has no defects, one that is very similar looking with small changes and one that has a more immediate tell in the difference, showcasing how good the algorithms tell apart each class using two different size of datasets, while also providing information on training times and different restrained hardware implementation results. Providing results on implementation metrics as well as training metrics.
- Comparative study of mass-accommodation methods and energy balances for melting paraffin wax in cylindrical thermal energy storage systems(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-03) Silva Nava, Valter; Otero Hernández, José Antonio; Hernández Cooper, Ernesto Manuel; emimmayorquin, emipsanchez; Santiago Acosta, Rubén Darío; Melo Máximo, Dulce Viridiana; School of Engineering and Sciences; Campus Ciudad de México; Chong Quero, Jesús EnriqueThis study introduces two innovative methods for modeling how paraffin wax melts inside a centrally heated annular space. Both approaches tackle the challenge of volume changes during melting by ensuring total mass is conserved, keeping the material mass constant, and adding a new equation of motion. To manage these volume shifts in a cylindrical setup, one method allows the outer radius to expand or contract radially, while the other treats the extra liquid volume as a dynamic variable along the central axis. Each method’s energy–mass balance at the boundary between the liquid and solid yields equations that describe how the interface moves, with only slight differences that still respect mass conservation. When melting occurs rapidly, the steady-state values for both volume and interface position are directly linked to the densities of the liquid and solid forms. The methods were put to the test in a vertical annular region filled with para!n wax, where thermodynamic properties were fine-tuned by minimizing the gap between measured and predicted temperatures. The widely used local energy balance at the melting front can sometimes mislead, depending on starting conditions, boundaries, and material traits. In contrast, the total energy balance method aligns closely with equilibrium, as shown by its agreement with thermodynamic equilibrium in saturated mixtures, and it delivers much smaller errors than the local approach. In a melting experiment using para!n RT50 inside a thermally insulated cylinder, the local energy balance underestimated the melting front position by 2.4% to 6.9%, whereas the total energy balance method kept discrepancies between 0.28% and 5.71%.