Varying Process Conditions Research Articles

Artificial intelligence assisted prediction of optimum operating conditions of shell and tube heat exchangers: A grey‐box approach

AbstractIn this study, a Grey‐box (GB) model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger (STHE) under varying process conditions. Aspen Exchanger Design and Rating (Aspen‐EDR) was initially used to construct a first principle model (FP) of the STHE using industrial data. The Genetic Algorithm (GA) was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions. A dataset comprised of optimum process conditions was generated through FP‐GA integration and was utilised to develop an Artificial Neural Networks (ANN) model. Subsequently, the ANN model was merged with the FP model by substituting the GA, to form a GB model. The developed GB model, that is, ANN and FP integration, achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model. Performance of the GB framework was also comparable to the FP‐GA approach but it significantly reduced the computation time required for estimating the optimum process conditions. The proposed GB‐based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.

Near-surface microstructures convolute mechanical properties in additively manufactured metals

Kovar specimens were additively manufactured with 180 variations in process conditions. Three distinct geometries (two tensile geometries and a Charpy specimen) were evaluated for each set of process conditions. Tensile specimens additively manufactured to net shape had less porosity, more uniform material properties, higher average ductility, and they consistently failed via ductile rupture. Tensile specimens harvested via electric discharge machining from larger additively manufactured blocks often contained lack of fusion voids throughout the cross section - those pre-existing pores drove pre-mature failure. As-printed specimens, on the other hand, were more representative of the outer border properties rather than the interior of large printed parts. The properties of the specimens cut from the larger block of additively manufactured material were more representative of the inner hatch properties but were stochastic, depending on the size and location of present voids. Using a high-throughput methodology, the results from over 800 tensile tests are reported here. This extensive statistical sampling allows the effects of specimen type and location to be clearly distinguished from other intentional variables (process parameter variations) and stochastic material variability.

Modeling of adsorptive treatment of lead (II) ions contaminated effluents using cashew nut shell alkali activated carbon: Batch kinetic, isothermal and thermodynamic studies

The numerical study of the decontamination of lead(II) ions-rich effluent employing activated carbon derived from cashew nut shell was the central focus of this work. Chemical activation with zinc chloride (ZnCl2) was used to produce powdered activated carbon at optimized activation conditions of impregnation ratio (0.531), activation temperature (1083 K) and activation time (63min). The physiochemical properties of raw and activated cashew nut shell were determined using Fourier transform infrared spectroscopy, scanning, X-ray diffractor (XRD), scanning electron microscopy (SEM), and Brunauer-Emmet-Teller (BET) analyses. Batch adsorption experiments were performed at varied process conditions of temperature (303–323) K, contact time (5–80min), and initial lead(II) ion concentrations (100–400 mg.L−1). The adsorption behavior of the batch–type operation was mathematically described in the form of non-linear parabolic partial differential equations (PDE’S) incorporating film, pore and surface diffusion mechanisms. The batch mathematical model was solved by implementing a custom MATLAB program-OkiyNwabannebatch and calibrated to predict the desired response (percentage removal) using measured adsorption data. The equilibrium adsorption, kinetics and thermodynamics of lead(II) ions adsorption onto activated cashew nut shell were also assessed using nonlinear isotherm, kinetic and thermodynamic models. Sensitivity analysis unveiled that temperature with sensitivity value of 37.31 % had a predominant effect on the model performance. The simulated equilibrium data was well explicated by the Freundlich model for lead(II) ions adsorption onto activated cashew nut shell. The pseudo-second order kinetic model best reproduced the simulated adsorption data for lead(II) ions uptake by cashew nut shell adsorbent. Likewise, the kinetic data followed Boyd model, indicating that the adsorption process is controlled by external (film) diffusion for the uptake of lead(II) ions by alkali activated cashew nut shell. Thermodynamic analysis using the Gibbs and Van’t Hoff’s models indicated that lead(II) ions adsorption by modified cashew nut shell is spontaneous and exothermic. The physical nature of the adsorption process was elucidated from the low values of the isosteric heats of adsorption (ΔHX) evaluated (<80 kJ mol−1). This study showed that zinc chloride activated cashew nut shell has good potential to be used as a cost-effective and efficient adsorbent for the uptake of lead(II) ions from aqueous solution.

Food process contaminants: formation, occurrence, risk assessment and mitigation strategies - a review.

Thermal treatment of food can lead to the formation of potentially harmful chemicals, known as process contaminants. These are adventitious contaminants that are formed in food during processing and preparation. Various food processing techniques, such as heating, drying, grilling, and fermentation, can generate hazardous chemicals such as acrylamide (AA), advanced glycation end products (AGEs), heterocyclic aromatic amines (HAAs), furan, polycyclic aromatic hydrocarbons (PAHs), N-nitroso compounds (NOCs), monochloropropane diols (MCPD) and their esters (MCPDE) which can be detrimental to human health. Despite efforts to prevent the formation of these compounds during processing, eliminating them is often challenging due to their unknown formation mechanisms. It is critical to identify the potential harm to human health in processed food and understand the mechanisms by which harmful compounds form during processing, as prolonged exposure to these toxic compounds can lead to health problems. Various mitigation strategies, such as the use of diverse pre- and post-processing treatments, product reformulation, additives, variable process conditions, and novel integrated processing techniques, have been proposed to control these food hazards. In this review, we summarize the formation and occurrence, the potential for harm to human health produced by process contaminants in food, and potential mitigation strategies to minimize their impact.

Mimicking Polymer Processing Conditions on the Meso-Scale: Relaxation and Crystallization in Polyethylene Systems after Uni- and Biaxial Stretching.

Highly varying process conditions drive polymers into nonequilibrium molecular conformations. This has direct implications for the resulting structural and mechanical properties. This study rigorously investigated processing-property relations from a microscopic perspective. The corresponding models use a mesoscale molecular dynamics (MD) approach. Different loading conditions, including uniaxial and biaxial stretching, along with various cooling conditions, were employed to mimic process conditions on the micro-scale. The resulting intricate interplay between equi-biaxial stretching, orientation, and crystallization behavior in long polyethylene chains was reviewed. The study reveals notable effects depending on different cooling and biaxial stretching procedures. The findings emphasize the significance of considering distributions and directions of chain ordering. Local inspections of trajectories unveil that crystal growth predominantly occurs in regions devoid of entanglements.

Investigating battery black mass leaching performance as a function of process parameters by combining leaching experiments and regression modeling

The current paper investigates the leaching phenomena of industrially produced Li-ion battery waste in hydrometallurgical recycling processes. Specifically, it studies the leaching reactions of NMC111-type (LiNi1/3Mn1/3Co1/3O2) black mass, as well as the statistical behavior of cathode material leaching yields under varying process conditions. The investigated process variables include reductive agent concentrations (Fe2+, Cu, H2O2) as well as process temperature, whereas S/L ratio (200 g/L) and initial acidity (2 M H2SO4) were kept constant. At lower temperatures (T = 30 °C), copper was found to act as the predominant reductant, enabled by the presence of sufficient solution iron concentrations (≥0.4 g/L Fe). Conversely, at higher temperatures (T ≥ 50 °C), the reductive capability of aluminum was substantially increased due to its decreased tendency for passivation. In contrast to copper, dissolved iron did not notably affect the reductive behavior of aluminum. The efficiency of metallic reductants initially present within the black mass was high, reaching cathode metal leaching yields above 90 % at 70 °C. A predictive leaching model for black mass leaching yields was built via regression analysis and can be used to indicate pregnant leach solution (PLS) composition – via leaching yields – after two hours of processing as a function of the investigated process variables. The model showed leaching temperature to be the most impactful parameter, while also indicating a higher reductive efficiency of copper when compared to equimolar additions of H2O2. As a case example, LFP (LiFePO4) cathode powder was also investigated as an alternative reductant/catalysis species (Fe2+) source in the system and was found to increase cathode metal leaching yields almost as much as FeSO4 while also resulting in a remarkable increase in Al dissolution in the process.

Effect of processing parameters and thermal history on microstructure evolution and functional properties in laser powder bed fusion of 316L

In this paper, we explain and quantify the causal effect of processing parameters and part-scale thermal history on the evolution of microstructure and mechanical properties in the laser powder bed fusion additive manufacturing of Stainless Steel 316L components. While previous works have correlated the processing parameters to flaw formation, microstructures evolved, and properties, a missing link is the understanding of the effect of thermal history. Accordingly, tensile test coupons were manufactured under varying processing conditions, and their microstructure-related attributes, e.g., grain morphology, size and texture; porosity; and microhardness were characterized. Additionally, the yield and tensile strengths of the samples were measured using digital image correlation. An experimentally validated computational model was used to predict the thermal history of each sample. The temperature gradients and sub-surface cooling rates ascertained from the model predictions were correlated with the experimentally characterized microstructure and mechanical properties. By elucidating the fundamental process-thermal-structure–property relationship, this work establishes the foundation for future physics-based prediction of microstructure and functional properties in laser powder bed fusion.

Modulating the morphology and protonation degree of chitosan coatings for enabling controlled fabric functionalization

Chitosan (CS), a versatile biopolymer known for its diverse bioactivity, is commonly used as a coating or finishing agent to enhance fabric performance and broaden their applications. However, conventional methods for introducing CS coating layer on the fabrics often relies on using composition to control their functionality, limiting the ability to tailor properties for specific needs. In this study, we introduce an innovative coating platform utilizing drying, ionic gelation, or a combination of both to control the morphology and amino-protonation degree of CS. This approach allows the development of three distinct functional CS coatings from the same material composition. The method is effectively applied to cotton and polylactic acid (PLA)-based non-woven fabrics, preserving the permeability, breathability, and mechanical properties of the substrates. By varying processing conditions, it is found that CS coatings can impart distinct functions to the fabrics upon their morphology, such as attaining high degree of hydrophobicity (with a water contact angle reaching 129°), broad-spectrum antibacterial activity (>99.99 %) for the sample prepared by combined drying and ionic gelation methods. Additionally, our CS-coated fabrics can exhibit good biocompatibility, as demonstrated by cytocompatibility and histocompatibility evaluations, while excellent anti-adhesive properties and healing performance in infected wound models in rats can be obtained with the presence of CS microsphere. Our methods are straightforward and potentially scalable, enabling modular control over the functionality of CS-coated fabrics to meet diverse requirements. This work offers a promising strategy for modifying fabric properties with CS coatings, making it suitable for various applications such as facial masks, dressings, and antibacterial textiles.

Comparing Calcium Sulfate Fouling on Polymeric and Metal Heat Transfer Surfaces

AbstractFouling mitigation provides many challenges in process equipment, especially in heat exchangers. In this work, the fouling behaviors of two polymers are compared to 1.4301 stainless steel (SS) in calcium sulfate solutions. An experimental setup that allows to classify the materials quickly and under varying process conditions is used to determine the fouling behavior. Results are compared to a screening apparatus which can test up to three materials simultaneously. Finally, the cleaning behavior of all three materials is investigated. Results show that polyether ether ketone (PEEK) is superior to SS in mitigating fouling and is easier to clean; however, polyether ether ketone with 30 % talcum (TKT) is not. This is attributed to the surface roughness of the materials.

Investigation of Sub-Bandgap Emission and Unexpected n-Type Behavior in Undoped Polycrystalline CdSexTe1-x.

Se alloying has enabled significantly higher carrier lifetimes and photocurrents in CdTe solar cells, but these benefits can be highly dependent on CdSexTe1-x processing. This work evaluates the optoelectronic, chemical, and electronic properties of thick (3µm) undoped CdSexTe1-x of uniform composition and varied processing conditions (CdSexTe1-x evaporation rate, CdCl2 anneal, Se content) chosen to reflect various standard device processing conditions. Sub-bandgap defect emission is observed, which increased as Se content increased and with "GrV-optimized CdCl2" (i.e., CdCl2 anneal conditions used for group-V-doped devices). Low carrier lifetime is found for GrV-optimized CdCl2, slow CdSexTe1-x deposition, and low-Se films. Interestingly, all films (including CdTe control) exhibited n-type behavior, where electron density increased with Se up to an estimated ≈1017cm-3. This behavior appears to originate during the CdCl2 anneal, possibly from Se diffusion leading to anion vacancy (e.g., VSe, VTe) and ClTe generation.

Influence of Pre- and Post-Contouring Strategies to Downskin Sloped Surfaces in Laser Powder-Bed Fusion (L-PBF) Additive Manufacturing.

Among various metal additive manufacturing (AM) technologies, L-PBF is known for fabricating intricate components. However, due to step edges and powder particle attachments, attaining a good surface finish is challenging, especially on downskin surfaces. Contour scanning has potential to improve surface quality because such scanning may dominate the surface formation of sloped features. This study evaluates the effects of pre- and post-contouring strategies on the sloped downskin surfaces fabricated using a commercial L-PBF system with Ti6Al4V powder. L-PBF parts printed at inclination angles 30°, 45° and 60° were investigated. A double-contouring approach with varying processing conditions was employed and surface characteristics were analyzed using data acquired by white light interferometry. The average surface roughness, Sa, surface skewness, Ssk, and percentage area of powder particles attached onto surfaces were statistically evaluated. The lowest Sa obtained for pre- and post-contoured samples is 14.08 µm and 18.88 µm, respectively. For both strategies, the combination of a low laser power and a high scan speed on the interface of downskin surface and underneath powder results in smoother surfaces. However, while comparing both strategies, pre-contouring gives better surface finish for samples built at similar processing conditions, with a difference of nearly 5 µm in Sa.

Kinetic evaluation of the uranyl peroxide synthetic route on morphology

Abstract An important challenge in utilizing particle morphology in nuclear forensic or fuel fabrication applications is understanding why differences in morphologies are observed following varying processing conditions. This is often due to competition and interplay between thermodynamic and kinetic influences. To that end, some of the kinetic influences in the uranyl peroxide precipitation reaction were evaluated and compared to thermodynamic influences studied previously. Metastudtite (UO2O2·2H2O) was synthesized from solutions of uranyl nitrate or chloride, and the reaction time was varied from 100 s to 230 min enabling an evaluation of kinetic and thermodynamic influences. The metastudtite was then calcined to U3O8, and all materials were analyzed by powder X-ray diffraction (p-XRD) and scanning electron microscopy (SEM). Analysis by p-XRD confirmed the sample purity of metastudtite and U3O8. SEM images were analyzed using the Morphological Analysis for Materials (MAMA) software to measure the size and shape of the nanoparticles for a statistical comparison between materials. Metastudtite produced at shorter reaction times exhibited a kinetically controlled shape by forming smaller and rounder particles than metastudtite produced at longer reaction times. Metastudtite produced at the longer reaction times exhibited differences between the uranyl nitrate and uranyl chloride routes with the nitrate exhibiting a more angular and faceted morphology than the chloride. Overall, the control of the supersaturation ratio (S) played a significant role in determining the morphology of the metastudtite. Morphological differences between the U3O8 confirmed the role of nanoparticle agglomeration in forming larger sintered particles. The results help demonstrate the importance of understanding particle formation mechanisms in the long-term development of morphology in nuclear forensics or in developing advanced fuels with specific characteristics.

Testing the disintegration and texture-related palatability predictions for orodispersible tablets using an instrumental tool coupled with multivariate analysis: Focus on process variables and analysis settings

Orodispersible tablets (ODTs) represent a growing category of dosage forms intended to increase the treatment acceptability for special groups of patients. ODTs are designed to rapidly disintegrate in the oral cavity and to be administered without water. In addition, ODTs are easy to manufacture using standard excipients and pharmaceutical equipment.This study adds to previously published research that developed an instrumental tool to predict oral disintegration and texture-related palatability of ODTs with different formulations. The current study aimed to challenge the predictive capacity of the models under variable process conditions. The studied process parameters with potential impact on the pharmaceutical properties, texture profiles, and palatability were the compression pressure, punch shape and diameter. Subsequently, for all the placebo and drug-loaded ODTs, the in vivo disintegration time and texture-related palatability were determined with healthy volunteers.Previously developed regression models were applied to predict the formulation's disintegration time and texture-related palatability characteristics of ODTs obtained under different experimental conditions. The influence of process variables on the predictive performance of the models was estimated by calculating the residuals as the difference between the predicted and observed values for the investigated response. Increasing the speed of the analyser`s probe from 0.01 mm/s to 0.02 mm/s led to an improved differentiation of the texture profiles. The in vivo disintegration time and texture-related palatability scores were only influenced by the mechanical resistance and the tablet shape. Lower score was observed for the larger diameter tablets (10 mm). Overall, the prediction of the disintegration time at 0.02 mm/s was more accurate, except for stronger tablets. The best prediction of texture-related palatability was achieved for the 10 mm tablets, tested at 0.01 mm/s speed.The same model achieved good predictions of the oral disintegration time for all API-loaded formulations, which confirmed the ability of the texture analysis to capture process-related variability. Drug loading decreased the predictive capacity of the texture-related palatability because of the taste effect.

Identification of an affordable and printable metastable β Ti alloy with outstanding deformation behaviour for use in laser powder bed fusion

Research on metastable β Ti alloys has recently shown the possibility to achieve outstanding ductility and work hardening behaviours by engineering the materials’ unique strain transformable attributes and responses to heat treatment. As quenching is central to the development of the metastable phase in these alloys, it is of interest to understand how these materials could be designed and developed for use in laser additive manufacturing where feedstocks experience numerous rapid thermal cycles. In this context, we first propose a material design framework to identify printable metastable Ti alloys for laser powder bed fusion (L-PBF). We focus on the ternary Ti-Cr-Sn system for both its affordable character and interest in the biomedical field. The design framework combines criteria based on well-established empirical parameters that we use to compare stability of β phase and resistance to cracking during L-PBF, i.e. its printability. The design work is supported by series of microstructural investigations on arc-melted buttons of selected compositions. Using such methodology we identify Ti-7.5Cr-4.5Sn as strain transformable alloy of good printability and we then characterise this alloy in depth in a series of laser powder bed fusion experiments. Printability of this alloy is further demonstrated via printing experiments using a variety of processing conditions while the deformation character is studied using compressive testing. It was found that the identified Ti-7.5Cr-4.5Sn alloy can achieve a high compressive strain exceeding 70 % thanks to deformation mechanisms that combine dislocation slip and {3 3 2}<1 1 3> twinning. This research paves the way towards the identification of novel Ti alloys for L-PBF beyond those used in the aerospace sector, opening the way for a broader field of applications.

Data-driven prediction of inter-layer process condition variations in laser powder bed fusion

The pressing problem of laser powder bed fusion of metals (PBF-LB/M) is the inability to predict and mitigate undesired process conditions in a build. This study aims to develop an efficient and geometric-agnostic prediction tool for the variations of the average meltpool temperature of each layer given a laser scanning path. The approach is a data-driven model based on defined physics-based features and measurement data from a two-color coaxial photodiode system. Although the coaxial photodiode system cannot measure absolute meltpool temperature, it can measure relative changes in the meltpool temperature if the process is performed in the shallow or no-keyhole regimes, the focus of this study. Once trained, the model can predict meltpool temperature variations before the start of the build process for a part geometry which had not yet been printed. The results show that variations in the layer average meltpool temperature can be predicted with the average coefficient of determination (r2) score of 0.65. Furthermore, the method shows the significance of considering the effect of surrounding parts on the average meltpool temperature profile of an observed part. For part geometries investigated in this study, the model shows the information from at least 40 previous layers is needed to enable sufficient and stabilized prediction performance.

Micro-macro modeling of tensile behavior of a friction stir welded hybrid joint of AlSi10Mg parts produced by powder bed fusion and casting

Additive manufacturing (AM) has gained considerable interest due to its ability to produce lightweight parts with hierarchical microstructures. However, the current constraints on the build chamber size in powder-bed fusion type AM processes limit its industrial application. A hybrid welded joint, consisting of an AM-processed and a conventionally manufactured part, can be employed to produce larger components. Due to the varying processing conditions, these hybrid welded joints contain a wide range of microstructural heterogeneities, which influences the mechanical properties of the joint. Using a numerical model to predict the mechanical behavior of welded joints by considering the microstructural variations is essential for the safe and reliable implementation of hybrid welded joints. This study aims to predict the local tensile behavior of each region of a hybrid friction-stir welded joint of AlSi10Mg produced by laser-based powder bed fusion and casting using a microstructure-sensitive model as well as the global tensile behavior by considering the properties of each region using a joint macroscopic model. The results from this modeling approach agree well with the experimental results. Therefore, this method can predict the mechanical behavior of hybrid welded joints and can establish the structure–property relationship in each weld region.

Estimation-based model predictive control of an electrically-heated steam methane reforming process

The surge in demand for hydrogen (H2) across diverse sectors, including clean energy transportation and chemical synthesis, underscores the need for a thorough investigation into H2 production dynamics and the development of effective controllers for industrial applications. This paper focuses on an electrically heated steam methane reforming (SMR) process for H2 production, offering advantages such as enhanced environmental sustainability, compactness, efficiency, and controllability compared to conventional reforming methods. Electric heating of the entire system allows for adjustments in current to control reactor temperature, thereby impacting hydrogen production rates. However, accurately modeling hydrogen production dynamics presents a formidable challenge, as complex models with high precision are computationally unsuitable for real-time control integration. Considering these factors, an accurate and efficient first-principles-based lumped-parameter model is developed to provide a dependable estimation of hydrogen production in an electrically-heated steam methane reformer. This model is validated experimentally and then utilized in a model predictive controller (MPC). To obtain the necessary state estimate information for the MPC, an extended Luenberger observer (ELO) method is employed to estimate state variables from limited, infrequent and delayed measurements of gas-phase reactor outlet stream and frequent measurements of the reactor temperature. Simulation comparisons with a proportional-integral (PI) controller reveal a much faster response in achieving the desired H2 production rate under the estimation-based MPC. Additionally, the simulations demonstrate the robustness of the controller to process variability such as a decrease in catalyst activation energy, commonly encountered in the SMR process, highlighting its effectiveness in maintaining stable operation under varying process conditions.

Numerical simulation of friction extrusion: process characteristics and material deformation due to friction

This study employs a finite element thermo-mechanical model, using a Lagrangian incremental setting to investigate friction extrusion (FE) under varying process conditions. The incorporation of rotation in FE generates substantial frictional heat, leading to significantly reduced process forces in comparison to conventional extrusion (CE). The model reveals the interplay between temperature, strain, and strain rate across different microstructural zones of the resulting wire. Specifically, the sticking friction condition in FE enhances initial shear deformation, aligning with a homogeneous spatial strain distribution and predicting complete grain refinement in the extruded wire, as per Zener-Hollomon calculations. On the other hand, under the sliding friction condition in FE, the shear deformation is reduced which results in an inhomogeneous microstructure in the extruded wire. The analysis of material flow in the workpiece reveals distinct transitions from the base material to the thermo-mechanically affected zones. The simulated process force, thermal history, and microstructure during sliding friction conditions align well with the findings from performed friction extrusion experiments.

Effect of heat treatment on functionally graded 304L stainless steel to Inconel 625 fabricated by directed energy deposition

Defining appropriate heat treatments for compositionally functionally graded materials (FGMs) is challenging due to varying processing conditions in terminal alloys and gradient regions. Therefore, it is critical to investigate the impact of potential heat treatments on phase transformations and the resulting mechanical properties along the entire compositional path. In the present study, we applied heat treatments at 700 °C, 900 °C, and 1150 °C on a stainless steel 304L (SS304L) to Inconel 625 (IN625) FGM fabricated using directed energy deposition (DED) additive manufacturing (AM). The microstructure and hardness, as a function of layer-wise composition and applied heat treatment, were characterized. The applicability of computational methods previously developed by the team to predict experimentally observed phases by the hybrid Scheil-equilibrium approach was evaluated. This approach improves the accuracy of predicting phases formed after heat treatment compared to equilibrium thermodynamic calculations using the overall layer compositions and provides a simple pathway to assist in designing heat treatment for FGMs.

Viscometry-based prediction of structural properties of high-moisture meat analogues using gelation properties of soy and pea isolate protein blends

In this study, utilizing the gelation properties of a blend containing soy protein isolate (SPI), pea protein isolate (PPI), and corn starch (CS), we introduced a novel approach to predict the structural attributes of high-moisture meat analogues (HMMA). By leveraging the “Rapid Visco Analyzer 4800″ viscometer, we measured the gelation behavior of SPI and PPI based on temperature profiles, underscoring a method to efficiently forecast fibrous structures and reduce trial errors in complex extrusion processes. As the PPI content increased, we observed a decline in the overall viscosity of the protein mixture, paralleled by a rise in color lightness. Contrary to the sturdy gel network formed by SPI, PPI showcased weaker particle bonds, indicating a structural vulnerability. In line with this, the texture of HMMA diminished as PPI content escalated. The secondary protein structure in both protein gels and HMMA revealed that as PPI content grew, there was a reduction in α-helix and β-sheet structures while random coil structures increased. The molecular strength of the protein gel waned with higher PPI content. Similarly, in HMMA, elevated PPI levels corresponded to fewer protein interactions. The study demonstrates that HMMA's textural properties can be effectively predicted by understanding the gelation characteristics of the SPI-PPI blend, highlighting the need for further research to refine prediction models that incorporate the complexities of molecular interactions in varying processing conditions.