Numerical modeling of particle deposition in a realistic respiratory airway using CFD-DPM and genetic algorithm.

In this study, a realistic model of the respiratory tract obtained from CT medical images was used to solve the flow field and particle motion using the Eulerian-Lagrangian approach to obtain the maximum particle deposition in the bronchial tree for the main purpose of optimizing the performance of drug delivery devices. The effects of different parameters, including particle diameter, particle shape factor, and air velocity, on the airflow field and particle deposition pattern in different zones of the lung were investigated. In addition, a genetic algorithm was employed to obtain the maximum particle deposition in the bronchial tree and the effect of the aforementioned parameters on particle deposition. Reverse flow, vortex formation, and laryngeal jet all affect the airflow structure and particle deposition pattern. The mouth-throat region had the highest deposition fraction at various flow rates. A change in the deposition pattern with an increased deposition fraction in the throat was observed owing to the increased diameter and shape factor of the particles, resulting from the higher inertia and drag force, respectively. The particle deposition analysis showed that three parameters, shape factor, diameter, and velocity, are directly related to particle deposition, and the diameter is the most effective parameter for particle deposition, with an effect of 60% compared to the shape factor and velocity. Finally, the prediction of the genetic algorithm reported a maximum particle deposition in the bronchial tree of 17%, whereas, based on the numerical results, the maximum particle deposition was reported to be 16%. Therefore, there is a 1% difference between the prediction of the genetic algorithm and the numerical results, which indicates the high accuracy of the prediction of the genetic algorithm.

Optimization studies on laccase activity of Proteus mirabilis isolated from treatment sludge of textile industry factories : Optimization of laccase activity of Proteus mirabilis.

Laccase is an exothermic enzyme with copper in its structure and has an important role in biodegradation by providing oxidation of phenolic compounds and aromatic amines and decomposing lignin. The aim of this study is to reach maximum laccase enzyme activity with minimum cost and energy through optimization studies of Proteusmirabilis isolated from treatment sludge of a textile factory. In order to increase the laccase enzyme activities of the isolates, medium and culture conditions were optimized with the study of carbon (Glucose, Fructose, Sodium Acetate, Carboxymethylcellulose, Xylose) and nitrogen sources (Potassium nitrate, Yeast Extract, Peptone From Soybean, Bacteriological Peptone), incubation time, pH, temperature and Copper(II) sulfate concentration then according to the results obtained. Response Surface Method (RSM) was performed on six different variables with three level. According to the data obtained from the RSM, the maximum laccase enzyme activity is reached at pH 7.77, temperature 30.03oC, 0.5 g/L CuSO4, 0.5 g/L fructose and 0.082 g/L yeast extract conditions. After all, the laccase activity increased 2.7 times. As a result, laccase activity of P. mirabilis can be increased by optimization studies. The information obtained as a result of the literature studies is that the laccase enzymes produced in laboratory and industrial scale are costly and their amounts are low. This study is important in terms of obtaining more laccase activity from P.mirabilis with less cost and energy.

Enhancing sustainability in self-compacting concrete by optimizing blended supplementary cementitious materials.

Within concrete engineering, the uptake of self-compacting concrete (SCC) represents a notable trend, delivering improved workability and placement efficiency. However, challenges persist, notably in achieving optimal performance while mitigating environmental impacts, particularly in cement consumption. However, simply reducing the cement content in the mix design can directly compromise the structural-concrete requirements. Towards these challenges, global trends emphasize the utilization of appropriate waste materials in blended concrete. This study explored a promising strategy by integrating supplementary cementitious materials (SCMs) to contribute to the United Nations' Sustainable Development Goals (SDGs) in addition to the engineering contributions. It suggests an optimal combination of Metakaolin (MK) and Limestone Powder (LP) to partially substitute cement. The research methodology employs the response surface method (RSM) to systematically explore the ideal ingredient ratios. Through a comprehensive analysis of orthogonal array of 16 mixes, encompassing both mixture and process variables, this study aims to explain the effects of MK and LP addition on the rheological and mechanical properties of SCC with varying cement replacement levels. In terms of mixture constituents, the total composition of cement, MK, and LP was fixed at 100%, while coarse aggregate (CA), fine aggregate (FA), and the water-to-binder ratio were held as process variables. In order to assess the rheological properties of the mix-design, various tests including slump flow, L-box, and sieve segregation were conducted. Additionally, to evaluate mechanical strength, samples were tested for compressive strength at both 7 and 28 days. Findings from the experiments reveal higher concentrations of MK result in reduced workability and hardened properties. Through RSM-based designed experimentation covering both rheological and mechanical aspects, it is observed that the optimal cement replacement level lies between 40 and 55%. The findings of this study contribute to the advancement of sustainable and structurally robust concrete practices, offering insights into the optimal utilization of SCMs to meet both engineering requirements and environmental sustainability goals.

Response Surface Methodology Optimization of Resistance Welding Process for Unidirectional Carbon Fiber/PPS Composites.

The use of thermoplastic composites (TPCs) as one of the lightweight solutions will inevitably encounter problems in connection. Resistance welding has the characteristics of high strength, simplicity, and high reliability, and is considered a very potential hot-melt connection technology. The resistance welding technology of unidirectional carbon fiber-reinforced polyphenylene sulfide composites (UCF/PPS) was systematically studied. The experimental results show that the 100-mesh brass mesh has the best resin wetting effect and heating efficiency, and the PPS/oxidized 100-mesh brass mesh composite resistance element (Ox-RE/PPS) has the highest welding strength. The welding failure mode changes from interface failure and RE failure to interlayer structure damage and fiber fracture. The single-factor experimental results show that the maximum welding strength is reached at 310 °C, 1.15 MPa, and 120 kW/m2. According to the conclusion of the single-factor experiment, the Box-Behnken method was further used to design a three-factor, three-level experiment, and a quadratic regression model was established according to the test results. The results of variance analysis, fitting curve analysis, and perturbation plot analysis proved that the model had high fitting and prediction abilities. From the 3D surface diagram analysis, the influence of power density is the largest, and the interaction between welding temperature and power density is the most significant. Combined with the analysis of Design Expert 13 software, the optimal range of process parameters was obtained as follows: welding temperature 313-314 °C, welding pressure 1.04-1.2 MPa, and power density 124-128 kW/m2. The average strength of resistance welding joints prepared in the optimal range of process parameters was 13.58 MPa.

Response surface optimization of sludge dewatering process: synergistic enhancement by ultrasonic, chitosan and sludge-based biochar.

Due to the colloidal stability, the high compressibility and the high hydration of extracellular polymeric substances (EPS), it is difficult to efficiently dehydrate sludge. In order to enhance sludge dewatering, the process of ultrasonic (US) cracking, chitosan (CTS) re-flocculation and sludge-based biochar (SBB) skeleton adsorption of water-holding substances to regulate sludge dewaterability was proposed. Based on the response surface method, the prediction model of the specific resistance to filtration (SRF) and sludge cake moisture content (MC) was established. The US cracking time and the dosage of CTS and SBB were optimized. The results showed that the optimal parameters of the three were 5.08 s, 10.1 mg/g dry solids (DS) and 0.477 g/g DS, respectively. Meantime, the SRF and MC were 5.4125 × 1011 m/kg and 76.8123%, which significantly improved the sludge dewaterability. According to the variance analysis, it is found that the fitting degree of SRF and MC model is good, which also confirms that there is significant interaction and synergy between US, CTS and SBB, and the contribution of CTS and SBB is greater. Moreover, the process significantly improves the sludge's calorific value and makes its combustion more durable.

Optimization of operating parameters and microbiological mechanism of a low C/N wastewater treatment system dominated by iron-dependent autotrophic denitrification.

Ferrous iron (Fe2+) reduces the amount of external carbon source used for the denitrification of low-C/N wastewater. The effects of key operating parameters on the efficiency of ferrous-dependent autotrophic denitrification (FDAD) and the functioning mechanism of the microbiome can provide a regulatory strategy for improving the denitrification efficiency of low C/N wastewater. In this study, the response surface method (RSM) was used to explore the influence of four important parameters-the molar ratio of Fe2+ to NO3--N (Fe/N), total organic carbon (TOC), the molar ratio of inorganic carbon to NO3--N (IC/N) and sludge volume (SV, %)-on the FDAD efficiency. Functional prediction and molecular ecological networks based on high-throughputs sequencing techniques were used to explore changes in the structure, function, and biomarkers of the sludge microbial community. The results showed that Fe/N and TOC were the main parameters affecting FDAD efficiency. Higher concentrations of TOC and high Fe/N ratios provided more electron donors and improved denitrification efficiency, but weakened the importance of biomarkers (Rhodanobacter, Thermomonas, Comamonas, Thauera, Geothrix and unclassified genus of family Gallionellaceae) in the sludge ecological network. When Fe/N > 4, the denitrification efficiency fluctuated significantly. Functional prediction results indicated that genes that dominated N2O and NO reduction and the genes that dominated Fe2+ transport showed a slight decrease in abundance at high Fe/N levels. In light of these findings, we recommend the following optimization ranges of parameters: Fe/N (3.5-4); TOC/N (0.36-0.42); IC/N (3.5-4); and SV (approximately 35%).

A New Method of Extracting Polygonatum sibiricum Polysaccharide with Antioxidant Function: Ultrasound-Assisted Extraction-Deep Eutectic Solvents Method.

Polygonatum sibiricum Polysaccharide (PsP) with antioxidant function is the main active component of Polygonatum sibiricum (P.sibiricum). The currently poor extraction yield and extraction methods of PsP cannot meet the application of that in food industrial production. In this research, an ultrasound-assisted extraction-deep eutectic solvents (UAE-DESs) method, which has never been used in the PsP industry, was first used to extract PsP. The extraction conditions were optimized by the response surface method (RSM). Both the extraction yield and antioxidant function were simultaneously considered during the optimization process. The indicators of PsP's level and antioxidant activity in vitro were used to present the extraction yield of the UAE-DESs method, the purity, and the antioxidant effect of PsP. Under the optimal conditions, which included a liquid-solid ratio of 26:1 (mL:g), extraction temperature of 80 °C, ultrasonic time of 51 min, and ultrasonic power of 82 W, the PsP extraction yield could reach (43.61 ± 0.09)%, which was obviously higher than single DESs (33.81%) and UAE (5.83%), respectively, and the PsP appeared favorably antioxidant function. This research proposed an efficient extraction method for PsP, filled the basic research gap, and further improved the development of PsP as a dietary supplement with antioxidant function in the food industry.

Development of response surface method prediction model for traffic-related roadside noise levels based on traffic characteristics.

Recently, several urban areas are trying to mitigate the environmental impacts of traffic, where noise pollution is one of the main consequences. Thus, studying the determinants of traffic-related noise generation and developing a model that predicts the level of noise by controlling the influencing factors are crucial for transportation planning purposes. This research aims at utilizing the response surface method (RSM) to develop a robust statistical prediction model of traffic-related noise levels and optimize different traffic characteristics' ranges to reduce the expected noise levels. The results indicate that the rate of Leq increase is higher at traffic flow values less than the 1204 veh/h. The interaction effect of flow-speed and flow-heavy vehicle percentage pairs shows that Leq has peak values around 45.8 km/h and 28.71%, respectively, with almost symmetric value distribution about those center points. The main effects study indicates a direct effect of traffic flow, speed, density, and traffic composition on roadside noise levels. The prediction model has good representativeness of observed noise levels by predicted noise levels as the model has a high coefficient of determination (R2 = 95.87% and R2 adj = 92.26%) with a significance level of 0.0036. Then, the research presents a methodology to perform an optimization of the roadside noise level by defining traffic characteristics that can keep the noise level below 65 dB(A) or minimize noise level. Decision-makers could use the proposed method to control the roadside noise level.

Optimization of Electrical and Mechanical Properties through the Adjustment of Design Parameters in the Wet Spinning Process of Carbon Nanotube/Polyvinylidene Fluoride Fibers Using Response Surface Methodology.

The optimal process conditions for fabricating carbon nanotube (CNT)/polyvinylidene fluoride (PVDF) fibers with varying properties using a wet spinning process were experimentally determined. A dope solution was prepared using multi-walled nanotubes, PVDF, and dimethylacetamide, and appropriate materials were selected. Design parameters affecting the chemical and physical properties of CNT/PVDF fibers, such as bath concentration, bath temperature, drying temperature, and elongation, were determined using a response surface method. The wet-spinning conditions were analyzed based on the tensile strength and electrical conductivity of the fibers using an analysis of variance and interaction analysis. The optimized process conditions for fabricating CNT/PVDF fibers with different properties were derived and verified through fabrication using the determined design parameters.

Bifunctional catalytic degradation of diclofenac over Cu-Pd co-modified sponge iron-based trimetal: Parameter optimization.

Currently, the pharmaceutical and personal care products (PPCPs) have posed great challenge to advanced oxidation techniques (AOTs). In this study, we decorated sponge iron (s-Fe0) with Cu and Pd (s-Fe0-Cu-Pd) and further optimized the synthesis parameters with a response surface method (RSM) to rapidly degrade diclofenac sodium (DCF). Under the RSM-optimized conditions of Fe: Cu: Pd = 100: 4.23: 0.10, initial solution pH of 5.13, and input dosage of 38.8 g/L, 99% removal of DCF could be obtained after 60 min of reaction. Moreover, the morphological structure of trimetal was characterized with high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), X-ray diffraction (XRD), X-ray photoelectron spectra (XPS). Electron spin resonance (ESR) signals have also been applied to capture reactive hydrogen atoms (H*), superoxygen anions, hydroxyl radicals, and single state oxygen (1O2). Furthermore, the variations of DCF and its selective degradation products over a series of s-Fe0-based bi(tri)metals have been compared. Additionally, the degradation mechanism of DCF has also been explored. To our best knowledge, this is the first report revealing the selective dechlorination of DCF with low toxicity over Pd-Cu co-doped s-Fe0 trimetal.

Modeling and optimization of the coagulation/flocculation process in turbidity removal from water using poly aluminum chloride and rice starch as a natural coagulant aid.

The application of the coagulation/flocculation process is very important due to its simplicity in removing turbidity. Due to the disadvantages of using chemical coagulants in water and the lack of sufficient effect of natural materials alone in removing turbidity for proper performance, the simultaneous use of chemical and natural coagulants is the best way to reduce the harmful effects of chemical coagulants in water. In this study, the application of poly aluminum chloride (PAC) as a chemical coagulant and rice starch as a natural coagulant aid to remove turbidity from aqueous solutions was investigated. Effects of the above coagulants on the four main factors, coagulant dose (0-10 mg/L), coagulant adjuvant dose (0-0.1 mg/L), pH (5-9), turbidity (NTU 0-50), and each five levels were assessed using a central composite design (CCD). Under the optimized conditions, the maximum turbidity elimination efficiency was found to be 96.6%. The validity and adequacy of the proposed model (quadratic model) were confirmed by the corresponding statistics (i.e., F-value of 23.3, p-values of 0.0001, and lack of fit of 0.877 for the model, respectively, R2 = 0.88, R2adj. = 0.84, R2 pred = 0.79, AP = 22.04).

Development and application of novel high throughput metal waste chips and foam electrodes for electrocoagulation treatment of graywater.

In this study, novel high throughput metal waste chips and foam electrodes were developed for the electrocoagulation of graywater for the first time. The developed electrodes were then compared with traditional metal plate electrodes, which showed higher efficiency of developed electrodes. The effective parameters of pH, electrode distance, applied voltage, and reaction time on COD removal were optimized using RSM as a multivariate optimization technique, and the data were analyzed by ANOVA, normal plot, residual distribution, and 3D plots. The optimal conditions for electrocoagulation of graywater using metal (Al) plate electrode were determined as a pH of 6.86, electrode distance of 5 mm, and applied voltage of 5 V for a reaction time of 10 min, resulting in 89.1% COD removal and 74% turbidity removal. Finally, the performance of aluminum plate electrodes, foam electrodes, and electrodes made from metal waste chips was compared using COD removal efficiency as the index, revealing 84%, 93%, and 87% COD removal, respectively. These results demonstrated that the newly developed electrodes are suitable for graywater treatment with excellent COD removal efficiency, metal chip waste recycling, and cost-saving.

Machine learning and statistical analysis for biomass torrefaction: A review.

Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kinetic analysis, process modeling, and computational fluid dynamics, have been used to explain torrefaction performance and characteristics. However, they may be insufficient in actual applications because of providing only some specific solutions. Machine learning (ML) and statistical approaches are powerful tools for analyzing and predicting torrefaction outcomes and even optimizing the thermal process for its utilization. This state-of-the-art review aims to present ML-assisted torrefaction. Artificial neural networks, multivariate adaptive regression splines, decision tree, support vector machine, and other methods in the literature are discussed. Statistical approaches (SAs) for torrefaction, including Taguchi, response surface methodology, and analysis of variance, are also reviewed. Overall, this review has provided valuable insights into torrefaction optimization, which is conducive to biomass upgrading for achieving net zero.

Research on Compressive Strength of Manufactured Sand Concrete Based on Response Surface Methodology.

Due to the impact of economic and social development on the environment, there is an increasing demand for manufactured sand to replace natural sand as fine aggregate for concrete. At the same time, the effect of admixtures on the rheological properties and compressive strength of concrete is crucial in civil engineering applications. In this study, with the Box-Behnken test model, we analyzed and investigated the impact of a composite admixture of stone powder (SP), pulverized fuel ash (PFA), and silicon fume (SF) on the compressive strength of siliceous manufactured sand concrete using response surface methodology (RSM). At the same time, the rheological properties of the siliceous artificial sand and river sand concrete were analyzed. The prediction of the compressive strength of siliceous artificial sand concrete was developed using multiple regression analysis, the factors of which were SP, PFA, and SF content, and the response value was compressive strength. Furthermore, response surface and contour lines were used to analyze the impact of composite admixtures. It is shown that the compounding of SP, PFA, and SF improve the rheological properties of manufactured sand concrete. For the single factor, SP has the greatest effect on the compressive strength of mechanism sand concrete and SF has the least effect. For compounding, SP and PFA have the most significant effect on the compressive strength of artificial sand shotcrete, and the compounding of PFA and SF have the least effect.

A Comparison and Analysis of Three Methods of Aluminum Crown Forgings in Processing Optimization.

In this study, three parameter optimization methods and two designs of experiments (DOE) were used for the optimization of three major design parameters ((bill diameter (D), billet length (L), and barrier wall design (BWD)) in crown forging to improve the formability of aluminum workpiece for shock absorbers. The first optimization method is the response surface method (RSM) combined with Box-Behnken's experimental design to establish fifteen (15) sets of parameter combinations for research. The second one is the main effects plot method (MEP). The third one is the multiobjective optimization method combined with Taguchi's experimental design method, which designed nine (9) parameter combinations and conducted research and analysis through grey relational analysis (GRA). Initially, a new type of forging die and billet in the controlled deformation zone (CDZ) was established by CAD (computer-aided design) modeling and the finite element method (FEM) for model simulation. Then, this investigation showed that the optimal parameter conditions obtained by these three optimization approaches (RSM, MEP, and multiobjective optimization) are consistent, with the same results. The best optimization parameters are the dimension of the billet ((D: 40 mm, the length of the billet (L): 205 mm, and the design of the barrier wall (BWD): 22 mm)). The results indicate that the optimization methods used in this research all have a high degree of accuracy. According to the research results of grey relational analysis (GRA), the size of the barrier wall design (BWD) in the controllable deformation zone (CDZ) has the greatest influence on the improvement of the preforming die, indicating that it is an important factor to increase the filling rate of aluminum crown forgings. At the end, the optimized parameters are verified by FEM simulation analysis and actual production validation as well as grain streamline distribution, processing map, and microstructure analysis on crown forgings. The novelty of this work is that it provides a novel preforming die through the mutual verification of different optimization methods to solve a typical problem such as material underfill.

The swelling performance of raw and modified bentonite of geosynthetic clay liner as the leachate barrier exposed to the synthetic E-waste leachate.

Heavy metals are prevalent in electrical and electronic waste. The sealing of this type of waste disposal site is critical due to the existence of toxic materials. In this regard, Geosynthetic Clay Liners (GCLs) are widely used as one of the most common engineered barriers at disposal sites. Recently, attention has been drawn to modifying the bentonite of GCL with polymers to improve barrier performance against leachate. The aim of this study was to evaluate the swelling performance of the raw and modified GCL with a hydrophilic anionic polymer called CarboxyMethyl Cellulose (CMC) with weight percentages of 8, 10, and 12 of dried bentonite against synthetic heavy metals' leachate, containing copper and zinc, simultaneously and separately, based on ASTM D5890. It was found that adding this polymer could improve the swelling rate of the GCLs. The optimum CMC rate for modified GCLs exposed to the cationic solutions, including copper and zinc, was estimated at 10%. It could also be noted that the swell index of both natural and modified bentonites against solutions, including two cations of copper and zinc, simultaneously, was more sensitive to the changes in zinc ion concentration versus copper metal concentration.

Optimization of the ultrasound-assisted extraction of flavonoids and the antioxidant activity of Ruby S apple peel using the response surface method.

A Box-Behnken Design (BBD) was employed to optimize the extraction of antioxidants from Ruby S apple peel by ultrasound-assisted extraction (UAE). The effect of extraction temperature (20-40 °C), extraction time (15-45 min), and ethanol concentration (50-90%) in water on extraction yield, total phenol content (TPC), total flavonoid content (TFC), and DPPH radical scavenging activity of Ruby S peel extracts (RPEs) were investigated. The optimized extraction conditions that maximized extraction yield, TPC, TFC, and DPPH radical scavenging ability, were temperature 20 °C, extraction time 25.30 min, and ethanol concentration 50%. The validity of designed model was verified, and experimental values obtained under optimum conditions concurred with predicted values. Hyperoside, isoquercitrin, and phloridzin, were among the major flavonoids extracted. Our findings demonstrate the suitability of UAE and RSM for the optimization of Ruby S peel extraction and suggest the potential use of RPEs as bioactive functional materials.

Improved glucose yield and concentration of sugarcane bagasse by the pretreatment with ternary deep eutectic solvents and recovery of the pretreated liquid.

In this study, a novel ternary deep eutectic solvents (DES) consisting of choline chloride/PEG/hydroxyethyl sulfonic acid (HSA) was developed to effectively improve glucose yield and concentration of sugarcane bagasse, and the conditions of the pretreatment were optimized by response surface method (RSM). Under the optimal conditions, the maximum glucose concentration (GC) could reach 12.39 g/L (HSA concentration 1.34 %, PEG400, 2.3 h, 150 °C), and the maximum glucose yield (GY) was 0.2497 g/g (HSA concentration 1.41 %, PEG400, 2.1 h, 150 °C). Hemicellulose was completely removed, and the maximum lignin removal rate was 86.89 %. After pretreatment, 95 % of the pretreated liquid can be recycled. Finally, the structural and morphological changes of bagasse before and after pretreatment were investigated by scanning electron microscopy (SEM), Fourier Transform infrared analyzer (FT-IR) and X-ray diffraction (XRD).

Microalgal-bacterial consortia for efficient wastewater treatment: Optimization using response surface methodology.

The performance of microalgal-bacterial consortia in wastewater treatment and biomass production needs to be further optimized to meet increasingly stringent effluent standards and operating costs. Besides, due to uncontrollability of ambient conditions, it is generally believed that operating conditions (e.g., aeration) respond to ambient conditions (e.g., illumination). Therefore, response surface methodology (RSM) based on Box-Behnken design was used in this study to analyze the removal of chemical oxygen demand (COD), NH3 -N and TP, and algal biomass of the microalgal-bacterial consortia within 48 h. The results showed that under medium illumination intensity (5000 lx), photoperiod (12:12) and aeration rate (0.55 L min -1 ), the removal efficiency of COD, NH3 -N and TP was the highest, and the maximal biomass growth rates were 95.43%, 95.49%, 89.42% and 99.63%, respectively. However, as the limited critical removal requirements of TP, the effluent standards can only be achieved within the small illumination intensity and photoperiod available range, even under medium aeration conditions, which means that under fixed operating conditions, the effective operation range will be very limited. In addition, based on RSM and differential equation analysis, the further study indicated that the effective treatment range can be greatly expanded within aeration responding, which meets the discharge standard of pollutants in China. PRACTITIONER POINTS: Illumination was responded by aeration for optimizing performance of microalgal-bacterial consortium for wastewater treatment and biomass productivity. The strategy of optimization was based on response surface methodology. The maximum effect on wastewater treatment and biomass productivity was based on partial differential equations and quadratic inhomogeneous equations. Limited to critical TP-removal requirements, effluent standards can meet only in the small-usable range of illumination, under medium aeration.

Optimal Design of Ferronickel Slag Alkali-Activated Material for High Thermal Load Applications Developed by Design of Experiment.

The development of an optimal low-calcium alkali-activated binder for high-temperature stability based on ferronickel slag, silica fume, potassium hydroxide, and potassium silicate was investigated based on Mixture Design of Experiment (Mixture DOE). Mass loss, shrinkage/expansion, and compressive and flexural strengths before and after exposure to a high thermal load (900 °C for two hours) were selected as performance markers. Chemical activator minimization was considered in the selection of the optimal mix to reduce CO2 emissions. Unheated 42-day compressive strength was found to be as high as 99.6 MPa whereas the 42-day residual compressive strength after exposure to the high temperature reached 35 MPa (results pertaining to different mixes). Similarly, the maximum unheated 42-day flexural strength achieved was 8.8 MPa, and the maximum residual flexural strength after extreme temperature exposure was 2.5 MPa. The binder showed comparable properties to other alkali-activated ones already studied and a superior thermal performance when compared to Ordinary Portland Cement. A quantitative X-ray diffraction analysis was performed on selected hardened mixes, and fayalite was found to be an important component in the optimal formulation. A life-cycle analysis was performed to study the CO2 savings, which corresponded to 55% for economic allocation.