Optimal Mixture Design Research Articles

Application of Mixture Design for Optimization of Smoke Signal Formulation: A Comparative Study of KClO3 and KNO3 as Oxidizers

Smoke signals are traditionally used in military contexts, but in recent times, they have gained popularity among civilians. The M18 smoke grenade was designed with a highly reactive oxidizer, KClO3, and substituting it with a safer oxidizer, notably KNO3, is one way that helps provide a safer choice for civilian use. However, providing optimal formulations for both formulations helps in deciding whether KNO3 can be substituted for the traditional KClO3 oxidizer. One of the techniques for enhancing smoke formulation is the Design of Experiments (DOE). Many researchers these days are focusing on substituting the smoke chemicals for a safer option using a trial-and-error process. However, from the standpoint of environmental contamination, numerical testing to identify the most significant output causes air pollution and chemical waste, which is not only costly but also endangers sea life if not properly disposed of. Therefore, it is crucial to optimize the smoke formulation in order to decrease waste and air pollution as well as enable future mass production of the product for both military and civilian use. The purpose of this paper is to implement the mixture design tool of the DOE approach to determine the optimal formulation of smoke signals using KClO3-based formulations, as well as to provide a comparative analysis of substituting KNO3 to optimize KClO3-based formulations in terms of time and smoke emission. From the KClO3-based formulation (28.68 wt.% KClO3, 23.47 wt.% C12H22O11, 34.39 wt.% dye, and 13.46 wt.% MgCO3) with an average time of 73.43 seconds, an acceptable formulation with a data means of 73.43, substituting with KNO3 oxidizer gave an average of 80.18 seconds, in which the smoke emission was slightly thinner compared to KClO3. As a result, KNO3 can be used as an alternative oxidizer to KClO3, and the KClO3-optimized formulation can be considered a baseline for smoke formulation for future research.

Hybrid extreme gradient boosting regressor models for the multi-objective mixture design optimization of cementitious mixtures incorporating mine tailings as fine aggregates

The design of cementitious mixtures incorporating mine tailings as fine aggregates is a multi-objective optimization (MOO) problem, in which both the uniaxial compressive strength (UCS) and cost of the mixtures need to be considered simultaneously. Given that data-driven methods have shown promising results when solving similar MOO problems, this study developed an extreme gradient boosting regressor (XGBR) model on a dataset extracted from the literature to predict the UCS. Among the efforts taken to improve the models, a genetic algorithm (GA)-based XGBR model demonstrated the optimal prediction performance, with an R2 of 0.959. Next, the GA-XGBR model and a cost equation were used as objective functions in the MOO problem. The non-dominated sorting genetic algorithm with elite strategy (NSGA-II) was selected to solve the optimization problem. A case study was conducted, generating mixture designs that offered improved trade-offs between cost and UCS compared to experimental designs. Finally, a graphical user interface was developed to provide access to the prediction model and optimization method. Overall, this work can be used as a guide for optimal mixture designs as it facilitates informed decision-making before the actual applications.

Optimal mixture design for organic Rankine cycle using machine learning algorithm

This study presents a new design tool for working fluid mixtures in organic Rankine cycles. The proposed tool comprises a blend model for the thermophysical properties of the formulated mixtures, an ORC modelto predict the performance of the mixtures in a specific application, and an optimizer based on the Bayesian inference method to identify the optimal mixtures compositions to be assessed. The tool is programmed to optimize an objective function based on predefined optimization targets. Importantly, the targets and their respective weights within the objective function can be adjusted to meet the specific requirements of the application under analysis, making this approach adaptable to diverse research and industrial objectives. The algorithm is applied to a case study to demonstrate its ability to define a low-GWP blend that can replace HFC-134a in a micro-scale ORC with recuperator, while maintaining and potentially enhancing performance. The optimization targets specified for the case study are the net power output, the net efficiency, the GWP and the blend size. Power and efficiency are computed through a validated model of the low-temperature ORC system used as benchmark case. The results showed that the procedure was able to formulate several blends that comply with the targets of the assigned task. Amongst the high-scoring mixtures, the most used pure fluids are R32, R152a, R1234yf, and R1234ze(E). The presence of HCs is limited to fewer mixtures, playing the main role of GWP-limiter. A method to estimate the flammability classification of the blends has been also applied, obtaining that most of them belong to the ASHRAE class 2l, except when an HC is present, in which case the fluid is may result in class 3.

Experimental Mixture Design for Optimization of CeO2/TiO2/Biochar Composite Catalyst for Enhanced Brewery Wastewater Treatment Using an Integrated Adsorption–Photocatalytic Process

Experimental Mixture Design for Optimization of CeO2/TiO2/Biochar Composite Catalyst for Enhanced Brewery Wastewater Treatment Using an Integrated Adsorption–Photocatalytic Process

Characterization of Taguchi optimized fiber reinforced copper slag concrete composites with strength properties

Establishing the ideal design has always been a difficulty for concrete engineers because of the inclusion of many alternative construction materials particularly when dealing with the incorporation of slag elements and fibers in concrete mix design. In this study, the Taguchi optimization method is implemented to predict the optimal mix design of fiber reinforced copper slag aggregate concrete (FRCSC) with mechanical strength properties. For this purpose, different parameters influencing mechanical strength were defined based on the available literature. The Taguchi approach was subsequently applied to develop an orthogonal array of parameters, and experimental tests were carried out in accordance with the suggested design array to acquire experimental data for each and every strength property. Three major influencing parameters like fiber length (FL), fiber percentage (FP), and copper slag content (CS) were considered in order to get optimal mix design for FRCSC. Analysis of variance was also employed to evaluate the effective factors on compressive, split-tensile, flexural, water absorption and void content properties for FRCSC. It was found that the proposed method was effective in finding the optimal mixture design from strength point of FRCSC and the experimental results indicated a significant improvement in five selected strength properties of FRCSC. From the results of Taguchi analysis, it can be concluded that, for five selected strength properties, 60% CS + 1% of fiber + 20 mm of fiber length is the optimal mix design combination for FRCSC. Finally, physical and microstructural characterization through FESEM and EDX were carried out to have a closer justification for the test results.

Multi-objective optimization of engineered cementitious composite based on machine learning and generative adversarial network

This study aims to establish a novel framework for mixture design optimization of engineered cementitious composite (ECC) by first collecting two original datasets of ECC's tensile stress and strain from the extensive and credible literature. The datasets comprise a wide range of variables including cementitious ingredients, 9 types of fiber and characteristics, admixtures, and experimental conditions. The data augmentation is then performed using a tuned constraints-modified Conditional Tabular Generative Adversarial Network (Tuned-CTGAN) to increase the model accuracy and generalizability. The fitness functions of tensile stress and strain are established based on four machine learning models with the hyperparameters tuned by the Hunger Games search (HGS) algorithm. After the data augmentation, the values of R2 in their test sets are increased from 0.874 to 0.925 and from 0.772 to 0.889, respectively. Subsequently, the third objective function (cost) is computed by polynomials and four classes of constraints (Min-max, volume, ratio, and fiber) are set up to define the variable's search space. A non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) is introduced to optimize the mixture proportions of ECC by simultaneously optimizing tensile stress, tensile strain, and cost. This paper combines the results of data augmentation, model prediction, and multi-objective optimization for complex ECC design, which aims to provide a basis for practical application.

Investigation of The Use of Nano Powder On Roller Compressible Fibrous Concrete Roads

Despite the fact that our country's road network is inferior to that of industrialized countries, the volume of motor traffic is increasing. Thus, optimal solutions are imperative during both project and construction phases of highway investments. Concrete superstructures offer an alternative to flexible superstructures in various regions globally, including airport runways, warehouses, urban roads, streets, highways and industrial buildings. The objective of this study was to enhance the compressive and flexural strength of concrete and provide resistance against abrasion. To achieve this, a roller compressible concrete (RCC) mixture design was developed by incorporating basalt fiber, nano TiO2, and nano Fe2O3 additives. The experimental results were subjected to statistical analysis via the technique of analysis of variance (ANOVA). The findings of the study indicated that the strength performance of the optimal RCC mixture design reinforced with nano powder and fibers was superior to that of conventional concrete. Upon examination on a material basis, it was determined that the compressive strength exhibited an increase when the water/binder ratio was utilised within the range of 0.38–0.4. As the utilisation of nano-Fe2O3 and basalt fiber materials increased, their compressive strength exhibited a corresponding enhancement. The incorporation of basalt fiber into the composite material resulted in an increase in flexural strength at all ratios where it was utilised. It can be concluded that basalt fiber has the greatest potential for enhancing flexural strength. The optimal water-to-binder ratio for flexural properties is between 0.34 and 0.38. While nano powders can have a positive impact, they cannot achieve the maximum level of strength. For improving abrasion resistance, it was established that reducing the water/cement ratio, using fly ash, basalt fiber, and nano Fe2O3 in the mixture decreased abrasion. However, the usage of nano TiO2 did not have a significant positive effect. The material with the least effect on compressive, flexural and abrasion resistance was identified as nano TiO2. When materials other than nano TiO2 are utilised in accordance with the established optimal mixture calculation, the durability of the resulting RCC road will reach the requisite standard.

An innovative moment balanced inference engine for predicting recycled concrete aggregate strength and minimizing mixture CO2 emissions

Recycled concrete aggregate (RCA) has an important role to play in reducing CO2 emissions and encouraging sustainable practices in the construction industry. This study introduces Optical Microscope Algorithm - Moment Balance Machine (OMA-MBM) as an innovative AI-based inference engine to improve the accuracy of RCA strength prediction. MBM improves the principles of Support Vector Machine (SVM) by considering moments to identify the optimal moment hyperplane. Backpropagation Neural Network (BPNN) is employed for weight assignment, and OMA is utilized for parameter optimization. The performance of the developed OMA-MBM was benchmarked against several machine learning models, including SVM, BPNN, PSO-SVM, and GWO-SVM, achieving the lowest error metrics with RMSE values of 9.711, 0.708, and 0.709 for compressive, flexural, and tensile strengths, respectively. It also scored the highest overall performance, represented by Reference Index of 1.000. Moreover, the model was successfully used to identify optimal RCA mixture designs in terms of achieving designated material strength requirements with the lowest CO2 emissions. The robustness of model predictions was further validated across diverse applications, i.e., Boston housing prices, vehicle fuel consumption, and building energy efficiency. These findings confirm the robustness and generalizability of OMA-MBM as an accurate predictive tool for development of sustainable construction.

Optimization of bacterial consortia for enhanced cadmium removal from contaminated environments: a case study near the Cochin Backwater System, Kerala, India

Heavy metal contamination is a major environmental and ecological threat worldwide, posing significant challenges. Excessive accumulation of heavy metal like cadmium in the environment poses significant risks to both the environment and human health, particularly in regions with industrial activities and insufficient waste management practices. In this study, we focused on optimizing bacterial consortia composed of Aeromonas hydrophila and Psychrobacter nivimaris for efficient removal of cadmium from contaminated environments, with specific emphasis on areas near the Cochin backwater system in Kerala, India. Sampling conducted at Sreebhoothapuram and Eloor Ferry Kadavu stations near Fertilizers and Chemicals Travancore (FACT) revealed these sites as having significant cadmium accumulation. Bacterial strains isolated from these stations exhibited notable resistance to cadmium, with levels reaching up to 40 mg/l and these strains maintained similar growth conditions, making them good candidates for constructing a bacterial consortium. The Cd-resistant bacteria were characterized and identified as Aeromonas hydrophila and Psychrobacter nivimaris. Through a synergetic approach, a consortium comprising two bacterial strains from different combinations obtained from these stations demonstrated promising cadmium resistance, reaching up to 60 mg/l. Mixture design optimization facilitated the determination of an optimal ratio (Aeromonas hydrophila: Psychrobacter nivimaris) = (0.329:0.671) for maximum removal efficiency. The scanning electron microscopy (SEM) analysis of both individuals and the consortium revealed morphological changes, such as modifications in the cell wall, shape, and size of the bacteria, that occur during the absorption of Cd (II). The efficiency of the optimized consortium was validated through Atomic Absorption Spectroscopy, achieving an impressive removal percentage of 96.5% for a real wastewater sample with a Relative Standard Deviation (RSD) less than 10%. This study underscores the potential of tailored bacterial consortia as effective bioremediation agents for Cd-contaminated environments, particularly in regions with elevated cadmium levels like the Cochin backwater system in Kerala.

AQbD-guided development and validation of an innovative extraction procedure and stability-indicating RP-HPLC method for quantification of posaconazole in tablet formulation

The objective of this work is to develop a stability-indicating HPLC method for the quantification of Posaconazole (PCZ) in tablet formulation using an Analytical Quality by Design (AQbD) approach. The development process involved the Design of Experiments (DOE) utilizing distinctive constraints mixture design for mobile phase ratio optimization and a 2-level factorial design for selection of extraction diluent compositions. Key responses measured included % assay and system suitability parameters. Method operable design regions (MODR) were determined, and final optimum conditions were selected. Forced degradation studies were conducted to assess method stability. The optimized HPLC method employed a Zorbax C18 column with a mobile phase consisting of pH 3.5 10mM phosphate buffer, acetonitrile, and methanol in a ratio of 30:53:17 % v/v/v. The method demonstrated stability-indicating capabilities, with PCZ degradation observed in acidic and oxidative environments, while remaining stable in alkali. Peak purity analysis from Empower software confirmed the absence of interaction with degradants. Validation according to ICH Q2 (R2) guidelines showed precision, linearity over the range of 0.25 µg/mL to 376 µg/mL, and accuracy demonstrated through recovery studies from 50 to 150%. The developed HPLC method utilizing AQbD approach is specific, robust, precise, and accurate for the quantification of PCZ in tablet formulations, thus suitable for routine analysis.

Optimization of geopolymer pervious concrete design using multi-phase discrete element modeling

This study aims to optimize mix designs of geopolymer pervious concrete using multi-phase discrete element modeling. Metakaolin based geopolymer concrete was prepared with different mix designs for laboratory testing. A multi-phase discrete element model (DEM) for pervious concrete was developed with randomly generated porous structure and realistic contact models between each component. Contact model parameters were calibrated using orthogonal analysis based on measurements of mechanical strengths and elastic modulus. Results of model validation indicate that the developed numerical model has good reliability to predict mechanical properties of pervious concrete with the influence of porosity. Both actual and simulated pervious concrete specimens present consistent crack propagation patterns and failure behavior. The optimization of mixture design is conducted by considering aggregate gradation and aggregate to paste ratio of geopolymer pervious concrete, which are based on experimental results and post-calibration simulations. The gradation with larger size aggregate is suitable for preparing geopolymer pervious concrete with higher permeability and tensile strength. More importantly, it is feasible to reduce paste content in geopolymer pervious concrete while maintaining similar or even higher mechanical strengths, which reduces the embedded carbon footprint. The study findings demonstrate the beneficial use of numerical modeling to determine the optimized mix design of composite material such as geopolymer pervious concrete.

A green binder for cold weather applications: enhancing mechanical performance of alkali-activated slag through modulus, alkali dosage, and Portland cement substitution

Below 5 °C, Portland cement (PC) experiences delayed hydration, slowing strength development, making it unsuitable for winter. Alkali-activated slag (AAS) emerges as a viable alternative with continuous hydration in low-temperature conditions. The effect of the activator nature on the performance of AAS cured at normal temperatures is well known, but further studies are required for low-temperature conditions. This study investigates the synergistic impact of activator modulus (1.2 and 1.5), alkali dosage (5, 7, and 9%), and PC substitution rates (0, 10, and 20%), on low-temperature cured AAS properties. Eighteen mixtures were prepared and cured at 2 °C. Compression and ultrasonic pulse velocity tests were conducted after 7, 28, and 90 days. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy analyses were performed to examine the microstructure of the samples. Elevating alkali dosage enhanced early age strength but resulted in a drop in 90-day strength. Simultaneous increases in modulus and PC substitution rate reduced strength due to shrinkage-induced crack formation. Optimal mixture design options included using 10% PC in the 1.2 modulus and omitting PC when the 1.5 modulus was selected. Despite low temperatures, the use of PC significantly accelerated the setting time. Altering modulus and alkali dosage caused a considerable change in the intensity of the peaks in the FTIR spectrum. The findings indicate that AAS shows promise when adjusting the mixture design for temperatures below 5 °C, which are unfavorable for the hydration of PC.

Combination of steam explosion pretreatment and mixture design optimization as a strategy to enhance the antioxidant activity of crude polysaccharides isolated from edible mushrooms

The edible polysaccharides derived from mushrooms are bioactive fibers that possess great health benefits. In particular, the association between the antioxidant activity of polysaccharides and their beneficial health effects has sparked significant interest. There is a focus on exploring novel strategies to enhance the antioxidant activity of mushroom polysaccharides. In this study, the combination of steam explosion pretreatment (an environmental friendly and efficient biomass pretreatment method) and mixture design optimization was used to enhance the antioxidant activity of polysaccharides from two edible mushrooms i.e., Agrocybe aegerita (AaP) and Agaricus bisporus (AbP). We found that steam explosion pretreatment increased the yield of AaP and AbP polysaccharides by 2.47 and 1.91 times, respectively. In addition, the steam explosion was found to augment the antioxidant capacity of crude polysaccharides through structural alterations. Moreover, the combination of steam explosion pretreatment and mixture design optimization further augmented the antioxidant activity of two extracted polysaccharides at optimal ratio (AaP: AbP = 1:4) in vitro. In summary, a combination of steam explosion pretreatment and mixture design optimization could be a promising and efficient approach to enhance the antioxidant activity of polysaccharides isolated from multiple types of mushrooms.

Optimization of wheat flour and purple sweet potato paste for texture, specific volume, and bread color using the mixture design method

Wheat flour is one of the important ingredients in bread processing because it contains gluten which greatly affects the character of the bread. In this research, wheat flour was substituted with purple sweet potato paste to reduce dependence on the use of wheat flour. Purple sweet potato has the potential for processing food products because of its nutritional content and very attractive natural purple color. This research uses the Optimal Custom Design Mixture Design Method which consists of two factors, namely wheat flour (40-90%) and purple sweet potato paste (10-40%) on 5 physical characteristic responses, namely texture, specific volume, color L, color a, and color b. The data were analyzed using expert design 13 with 8 formulations. The results show that the optimization of the wheat flour formulation 65.025% and purple sweet potato paste 34.975% is predicted to produce bread with a texture value of (texture analyzer) 2.945 N, specific volume 3.488 cm3/g, color L brightness level is 57.650%, color a is 3.410, and color b is 11.910 (manual method of color reader tool) with a desirability of 0.613.

Research on concrete early shrinkage characteristics based on machine learning algorithms for multi-objective optimization

Cracking phenomena in tunnel side wall structures (TSWS) increasingly jeopardize their longevity due to water leakage, reinforcement corrosion, and eventual collapse. The primary contributor, early-age shrinkage (EAS) induced by hydration reactions, significantly undermines structural stability and durability. The integration of expansion agents (EA) and fibers presents a low-cost, efficient strategy to counteract EAS-induced cracking. Despite its promise, limited research on the influencing factors constrains its broader application. This study delves into the impacts of EA content, the CaO–MgO ratio, and fiber reinforcement on flexural strength (FS), compressive strength (CS), and EAS, revealing a complex interplay where EA and CaO content detrimentally affect mechanical properties yet beneficially influence EAS. Results showed that EA and CaO content had negative effects on the mechanical properties, but had positive effect on EAS. Additionally, Random Forest (RF) was developed with hyperparameters refined via the firefly algorithm (FA) based on the experimental data. The validity of the built RF-FA models was verified by substantial correlation coefficients and low root-mean-square errors. Subsequently, a coFA-based firefly algorithm (MOFA) was proposed to optimize tri-objectives of mechanical properties, EAS, and cost simultaneously. The Pareto fronts were obtained effectively for the optimal mixture design. This study contributes to its practical implications, offering a scientifically grounded approach to enhancing TSWS concrete design for improved performance and durability.

Nanostructured lipid carrier loaded with Zingiber officinale oil to enhance transdermal bioactive delivery for topical formulation

This study reported on the enhancement of Zingiber officinale (Z. officinale) oil delivery, a bioactive with poor water solubility, by incorporating it into nanostructured lipid carriers (NLC) for transdermal bioactive delivery in topical formulations using the ultrasonication technique with a D–optimal mixture design approach. The independent factors in the process included the percentage of Z. officinale oil, solid lipid (glyceryl monostearate (GMS)), and liquid lipid (virgin coconut oil (VCO)). The study characterized the NLC– Z. officinale oil formulation based on particle size (PS), polydispersity index (PDI), zeta potential (ZP), and encapsulation efficiency (EE) as the dependent variables. Besides, Transmission Electron Microscopy (TEM), Fourier Transform Infrared Spectroscopy with Attenuated Total Reflection (FTIR–ATR), and Differential Scanning Calorimetry (DSC) were also employed. The optimized NLC–Z. officinale oil formulation, comprising 0.36 % Z. officinale oil, 0.14 % solid lipid (GMS), and 0.50 % liquid lipid (VCO). The study's results demonstrated that the finely tuned NLC– Z. officinale oil formulation, with an average diameter of 90.71 ± 9.48 nm, a PDI of 0.147 ± 0.01, a ZP of –45.7 ± 0.67 mV, and an EE of 88.80 ± 11.18 %, exhibited a controlled and sustained release profile following the Korsmeyer–Peppas model. The in vitro penetration studies on excised Sprague–Dawley rat skin and Strat–M® showcased the heightened penetration flux of NLC–Z. officinale oil compared to Z. officinale oil. Furthermore, in vivo distribution studies in female Sprague–Dawley rats underscored the deep penetration of NLCs into the dermal layer after 12 h of application. Notably, a 3–hour treatment with NLC–Z. officinale oil triggered the release of free fatty acids (FFA). The 90 days storage stability study revealed no significant changes in NLC– Z. officinale oil concerning PS and PDI, affirming its stability over time. These findings illuminated the potential applications of NLC– Z. officinale oil as a robust transdermal bioactive delivery system for Z. officinale oil. This underscored its significance in pharmaceutical formulations, providing a novel avenue for therapeutic purposes.

Enhancing the efficacy of nano-curcumin on cancer cells through mixture design optimization of three emulsifiers

Curcumin, a vital bioactive compound found naturally, has diverse biological applications. However, a major limitation of curcumin is its low bioavailability caused by its limited solubility in water. Hence, it is possible to overcome this problem through preparing oil in water nanodispersion of curcumin that emulsifier can play key role to produce nanodispersion. In the present study, the effect of three emulsifiers of Tween 80, Arabic Gum and Polyethylene glycol on preparing nanodispersions with desirable properties was investigated using subcritical water method and a mixture design. Zeta-potential and particle size of the achieved nanodispersions were taken into account as outcome factors. The optimum values for emulsifiers of Tween 80, Arabic Gum and Polyethylene glycol were obtained as 0.588 g, 0.639 g and 0.273 g, respectively, using the suggested model, so that obtained nanodispersion had minimum particle size (101.89 nm) and maximum zeta-potential (−24.99 mV). In fact, 102.5 nm and − 24.7 mV were obtained from experimental data at these values of emulsifiers. In addition, maximum loading potential (0.199 g/L), efficiency (99.5%), and minimum total curcumin loss (0.5%) were acquired at these optimum values. The results also show that the nanodispersion had a powerful antioxidant activity (65.27%) with extra antibacterial activity in facing with both E. coli and S. aureus strains. Moreover, curcumin nanodispersion was significantly taken up by HT-29 cells and resulted in the production of oxidative stress in the cells, leading to a decrease in the growth of cancer cells.

Determination of Viscosity, Syneresis and Acceptability of Optimized Yoghurt Analogues from African Yambean (Sphenostylis stenocarpa Hams) and Malted Red Rice (Oryza glabberima)

Yoghurt analogues were produced using African yambean (Sphenostylis stenocarpa Hams) and malted red rice (Oryza glabberima). Optimization of the mixture components and process factor was carried out using combined optimal mixture design, viscosity, syneresis and acceptability as responses were analysed. The fitted regression model for viscosity, syneresis and acceptability were all significant (P<0.05) with coefficient of determination (R2) values of 0.9992, 0.9983 and 0.9904 % respectively. The coefficient of variation (cv) values were 0.992, 6.58 and 6.58 % respectively. The lack-of-fit for the models were not significant (P < 0.05). The suitable mathematical models developed for the optimization of the fermentation variables in African yambean and malted red rice yoghurt is highly recommended for the development of a novel non-dairy probiotic yoghurt.

Effects of elevated ground temperatures on properties of cement grouts for deep rock grouting

AbstractAppropriate determination of the mix ratios of cement grouts is of vital importance to the quality of rock grouting and the risk reduction of groundwater inflow. The behavior of grout, often highly temperature dependent, is likely to be affected by the elevated ground temperature in deep rock masses. This paper aims to experimentally gain insights into the effects of elevated ground temperatures on the properties of cement grout in fresh and hardened states in deep rock grouting. The results revealed that a temperature of 35°C is crucial for changes in the properties of thick cement grout with a water–cement ratio of less than 0.8. When the temperature is up to 35°C, there can be significant improvements in rheological parameters, acceleration of grout setting, and increase in the rheological time dependence of thick cement grout; however, there may also be a slight impact on the initial grout flowability and the nature of shear thinning. The high temperature may still improve the stability of fresh cement grout and also improve the porosity and creep deformation of hardened cement grout considerably. The proposed constitutive model that couples the Burgers model with a fractional derivative‐based Abel dashpot in the series can be used to characterize the creep behavior of hardened cement grout appropriately. The paper provides a valuable reference for optimization of mixture design of cement grouts, thus enhancing deep rock grouting quality and improving safety.

Ultrasound bleaching and quality evaluation of rice bran oil

AbstractRice bran oil (RBO) contains rich nutrients that are beneficial to the human body, but RBO suffers from severe nutrient loss due to bleaching difficulties. This article investigates low‐power ultrasound‐assisted bleaching as a new method for RBO during the bleaching process, which could potentially reduce nutrient loss and lipid oxidation. First, florisil (FL), magnesium trisilicate (MAT), and activated blending earth (ABE) were selected to use a mixture design for formula optimization from six adsorbent types, and then, central composite design was employed to optimize the parameters of the bleaching process for the ultrasound‐assisted adsorbent formula. The results showed that the optimal formula was 66% FL, 10% MAT, and 24% ABE, the highest L* value and the lowest a* value is obtained at ultrasound power of 50 W for 9 min. The various parameters of RBO were subjected to comparison before and after ultrasound‐assisted bleaching. Following this bleaching procedure, the color and quality of RBO experienced notable enhancements, all the while maintaining micronutrient and fatty acid compositions within acceptable ranges. The characterization analysis of the adsorbent by FT‐IR spectroscopy and atomic force microscopy confirmed its adsorption of pigments and other substances. The results indicate that the low‐power ultrasound‐assisted bleaching is effective for RBO bleaching and may provide a promising alternative to the adsorption‐based bleaching method.Practical ApplicationsRBO is made from pressed rice bran and has high nutritional value, such as fatty acids, vitamin E, sterols, and oryzanol, with over 90% absorption by the body. The dark color of RBO makes it difficult to produce clear, transparent, and light‐colored rice bran oil, and even if bleaching is achieved, it results in significant nutrient losses. Conventional bleaching methods are commonly inefficient while emerging technologies cannot be applied industrially. Three adsorbents were selected for formulation optimization based on the fact that the bleaching effect of a single adsorbent is not obvious. Low‐power ultrasound‐assisted bleaching was performed to reduce nutrient loss and lipid oxidation. The aim is to find new bleaching methods that achieve bleaching results with acceptable nutrient losses.