Mixture Parameters Research Articles

Experimental study on buildability and mechanical properties of 3D printing cob

The application of 3D printing technology to earth buildings conforms to the development concept of low carbon, green and intelligent construction. The key issue lies in the development of earth material with both favorable buildability and sufficient mechanical properties. In this study, the printing parameters of mixture for 3D printing with different water contents were optimized. The optimum water content of 3D printing cob was determined based on the buildability. The result shows that when the flow spreading diameter is between 140 mm and 160 mm, 3D printing cob has good buildability performance. The optimum water content is 24.6 %. In addition, anisotropic performance was analyzed by compressive strength tests from three orthogonal directions. In particular, according to the test results of single printing limit height and yield stress, the buildability of 3D printing cob wall was analyzed. The building strategy of the printing divided into multiple times was obtained.

Assessing the potential accuracy of a small-sized goniometer with extended dynamic range based on nuclear magnetic resonance

This paper evaluates potential accuracy characteristics of a small-sized goniometer based on nuclear magnetic resonance with an extended dynamic range. This required constructing a model of goniometer errors, estimating its accuracy based on this model, and formulating practical recommendations for the design of such a device based on the accuracy assessment. To evaluate the accuracy of a nuclear goniometer, a theoretical model was built that makes it possible to determine the optimal operating parameters of the cell gas mixture, the ranges of their permissible changes, the sensitivity of the goniometer, and the dependence of its characteristics on external and internal factors. In particular, the dependence of output signal of the device on the parameters of gas mixture and optical pumping has been determined. For a goniometer with a cell volume of 8 cm3, the optimum temperature is 130 °C, and the optimum intensity of the pumping radiation is 5 mW. The dependence of output signal on the measured angle of rotation was also established, as well as the noise and error dependence of the device on the permissible values of its parameters. Based on the model built, parameters of a goniometer with a cell volume of 8 cm3 were determined; the maximum angular sensitivity of such a goniometer with complete suppression of technical noise is δφsen=1.0 arcsec. The greatest contribution to the angle measurement error is from the instability of pumping power Ip (ΔIp/Ip=0.05) – 85 %, magnetic field B0 (ΔB0/B0=10-8) – 13 %, temperature T (ΔT/T=0,1) – 2 %. The goniometer under consideration corresponds to the medium accuracy class, δφtot≥10 arcsec. It could be used in optical manufacturing for operational control, calibration, and certification of optical products. To improve the angular accuracy of the goniometer, it is necessary to increase the stability of the laser pumping intensity

Raman‐Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations

ABSTRACTRaman spectroscopy, a robust and non‐invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in Saccharomyces cerevisiae fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell‐free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real‐time, on‐line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of S. cerevisiae‐based biopharmaceutical production.

Multi-scale mechanical and acoustic characterization of low noise pavements

Using data on in situ performance of low noise pavements, three well performing mixtures were selected: SDA 4, SDA 6, and SDA 8. These mixtures and the corresponding mastic (filler + bitumen + additive) were tested for their mechanical and acoustic performance in non-aged and aged states using a multi-scale approach (mm to km). Thereafter, an optimization protocol was applied to these mastics (mm-scale) and promising combinations were implemented at the mixture scale (cm-scale). As a result, the best performing mixture parameters were implemented in the m-scale slabs where the mechanical and acoustic performance were determined. The proposed work could uniquely combine knowledge from in situ long term and laboratory performance of low noise pavements. Using a traffic simulator and measurements before and after trafficking, the acoustic modeling showed that the modified mixtures’ noise levels were maintained after loading. This indicates that the modification assured acoustical integrity of the mixture after loading.

Exploring the fundamental properties and MGF of a 3-component mixture of power distributions: a simulation study

This paper estimates the parameters of the 3-component mixture of power distributions. The expressions of fundamental properties of the said distribution are presented, and for judgment, the numerical results of mean, median, variance, mode and skewness are evaluated. The closed-form expressions of the reliability properties, the statistical properties and necessary inequality measures are discussed. The trend of reliability measures is shown with the help of numerical results and graphs by taking different values of parameters and proportion parameters. The mathematical expressions of certain related statistical functions are derived from the reliability functions, the moment generating function, the characteristics function, the probability generating function, and the factorial moment generating function. The expressions of essential entropies, Shannon’s entropy, the B-entropy and Renyi’s entropy, are derived. The mathematical expressions of inequality indexes, the Gini index, the Lorenz curve, the Bonferroni curve, the Zinga index, the Atkinson index and the generalized entropy index are also analysed. The RF, the HRF, the CHR, the RHR, the MRL, and the MWT functions are derived in reliability analysis. The numerical values and graphs for different parameters and proportion components are discussed to judge the behaviour of reliability properties.

A model of task-level human stepping regulation yields semistable walking.

A simple lateral dynamic walker, with swing leg dynamics and three adjustable input parameters, is used to study how motor regulation affects frontal-plane stepping. Motivated by experimental observations and phenomenological models, we imposed task-level multi-objective regulation targeting the walker's optimal lateral foot placement at each step. The regulator prioritizes achieving step width and lateral body position goals to varying degrees by choosing a mixture parameter. Our model thus integrates a lateral mechanical template, which captures the fundamental mechanics of frontal-plane walking, with a lateral motor regulation template, an empirically verified model of how humans manipulate lateral foot placements in a goal-directed manner. The model captures experimentally observed stepping fluctuation statistics and demonstrates how linear empirical models of stepping dynamics can emerge from first-principles nonlinear mechanics. We find that task-level regulation gives rise to a goal-equivalent manifold in the system's extended state space of mechanical states and inputs, a subset of which contains a continuum of period-1 gaits forming a semistable set: perturbations off of any of its gaits result in transients that return to the set, though typically to different gaits.

SCIENTIFICLY BASED CALCULATION METHODS AND EXPERIMENTAL DATA BASES FOR THE PREVENTION OF PREMIUM FIRE EXPLOSIONS AND UNSAFE EXPLOSIONS OF PYROTECHNIC MULTICOMPONENT NITRATE-METALLIZED MIXTURES UNDER EXTERNAL THERMAL

Problem statement. Methods of preventing the occurrence of forced fire-hazardous destruction of products in the event of exposure to external thermal actions are of great practical importance. At the same time, they should be based on scientifically based methods for determining the critical values of parameters of thermal effects on products and technological parameters of mixture charges, the excess of which leads to premature fire-hazardous destruction of products. To develop such methods, it is necessary to have the results of theoretical and experimental studies of the processes of heating the shells of mixture charges for various external heat flows and their thermoreaction times, as well as the processes of ignition and development of their combustion under different external conditions. Purpose of the article. To form the results of theoretical studies in the form of scientifically based methods that allow preventing and controlling the premature ignition of mixture charges, the explosive development of their combustion process and the fire-hazardous destruction of products under conditions of external thermal effects, as well as the compilation of the results of experimental studies into a single database with determination of the influence of the technological parameters of the mixtures on the characteristics of their ignition processes and the development of combustion, the use of which allows reducing the number of fire-explosive destruction of products in conditions of elevated heating temperatures and external pressures. Conclusion. Scientifically based methods of determining the critical values of the parameters of external thermal actions for the quantitative assessment of the level of fire safety of pyrotechnic products based on multi-component nitrate-metallized mixtures have been developed. A scientific and technical base of experimental data was created to determine the regularities of the influence of a wide class of technological parameters of mixtures on the main characteristics of the processes of ignition and development of combustion of mixtures (ignition temperature, induction time, speed of combustion development) under conditions of external thermal actions (elevated heating temperatures, external pressures, etc.), which makes it possible to determine the probability of fires occurring at facilities where products are stored.

Impacts of different hydrotropes on the aggregation behavior and physicochemical parameters of sodium dodecyl sulfate and ofloxacin drug mixture at several temperatures

Impacts of different hydrotropes on the aggregation behavior and physicochemical parameters of sodium dodecyl sulfate and ofloxacin drug mixture at several temperatures

Modeling and Mitigating Gas Hazards during Potash Mine Closure

The planned closure of potash mines achieved through the injection of highly mineralized brines into the worked-out area is a complex process. A critical concern arises when brines obstruct the aerodynamic connections between the flooded mine’s airspace and the atmosphere, potentially leading to the formation of closed cavities where explosive gases can accumulate. To address this hazard, it is imperative to develop systems capable of extracting the gas–air mixture from the unflooded part of the worked-out area. Two distinct scenarios are examined: the first involves controlled flooding with saturated brines, while the second contemplates flooding resulting from a hypothetical breakthrough of supra-salt strata, leading to the ingress of groundwater into the worked-out area. A novel mathematical model is introduced to predict the evolution of gas–air mixture parameters in the unflooded part of the worked-out area. Utilizing this model, we assess the effectiveness of proposed measures designed to eliminate explosive gases from the worked-out area. Specifically, a pipeline system is proposed for the removal of gases. The findings from this study contribute valuable insights into ensuring the safe and efficient closure of potash mines, shedding light on potential risks and effective mitigation strategies for gas-related hazards during planned flooding.

Experimental study and correlation of critical parameters for three binary mixtures containing R290 and hydrofluoroolefins

The mixtures of R290 (propane) and R1234yf (2,3,3,3-tetrafluoroprop-1-ene), R1243zf (3,3,3-trifluoropropene), or R1234ze(E) (trans-1,3,3,3-tetrafluoropropene) could be potential alternatives for high global warming potential (GWP) refrigerants hydrofluorocarbons (HFCs) and hydrochlorofluorocarbons (HCFCs). Obtaining the critical parameters of mixtures is crucial for establishing thermodynamic models, evaluating the highest operating temperature of refrigerants, determining the phase envelope, and confirming the starting point of the Widom line. However, few studies have been made on their critical properties. In this work, the critical properties of three binary systems containing R290 + R1234yf / R1243zf / R1234ze(E) are obtained experimentally with a metal-bellows volume apparatus. The critical state is judged by direct visual observation of critical opalescence and the recurrence of the vapor-liquid phase interface. The extended total uncertainties for the mole fraction, critical pressure, critical temperature, and critical density were below 0.004, 21 kPa, 50 mK, and 0.6 % (k = 2, 0.95 confidence coefficient), respectively. Experimentally obtained critical data are correlated by the Modified Wilson method and the Redlich–Kister method. The critical parameters of the R290 + R1234yf, R290 + R1243zf, and R290 + R1234ze(E) mixtures are predicted using the Correlated Modified Wilson (CMW) method, He et al.’s method, and the Modified Extended Chueh–Prausnitz (MECP) method. The correlated curve and predicted outcomes are employed for comparison with this work's experimental results. Meanwhile, the critical parameters data obtained experimentally are contrasted with the value of REFPROP 10.0 and other open literature. The fitting and prediction curves somewhat agree with the experimental results.

Diagnostics of the ionization processes in hydrocarbon flame with the use of the current-voltage characteristics

The possibility of estimating the ionization parameters of high-temperature gas mixtures formed as a result of combustion processes is considered on the basis of the current-voltage characteristics measured using electrodes that generate an external electric field in the media under consideration.

Bayesian improved cross entropy method with categorical mixture models for network reliability assessment

We employ the Bayesian improved cross entropy (BiCE) method for rare event estimation in static networks and choose the categorical mixture (CM) as the parametric family to capture the dependence among network components. The proposed method is termed BiCE-CM. At each iteration of BiCE-CM, the mixture parameters are updated through the weighted maximum a posteriori (MAP) estimate, which mitigates the overfitting issue of the standard improved cross entropy (iCE) method through a novel balanced prior, and we propose a generalized version of the expectation–maximization (EM) algorithm to approximate this weighted MAP estimate. The resulting importance sampling distribution is proved to be unbiased. For choosing a proper number of components K in the mixture, we compute the Bayesian information criterion (BIC) of each candidate K as a by-product of the generalized EM algorithm. The performance of the proposed method is investigated through a simple illustration, a benchmark study, and a practical application. In all these numerical examples, the BiCE-CM method results in an efficient and accurate estimator that significantly outperforms the standard iCE method and the BiCE method with the independent categorical distribution.

Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning

High-Performance Concrete (HPC) and Ultra-High-Performance Concrete (UHPC) have many applications in civil engineering industries. These two types of concrete have as many similarities as they have differences with each other, such as the mix design and additive powders like silica fume, metakaolin, and various fibers, however, the optimal percentages of the mixture design properties of each element of these concretes are completely different. This study investigated the differences and similarities between these two types of concrete to find better mechanical behavior through mixture design and parameters of each concrete. In addition, this paper studied the correlation matrix through the machine learning method to predict the mechanical properties and find the relationship between the concrete mix design elements and the mechanical properties. In this way, Linear, Ridge, Lasso, Random Forest, K-Nearest Neighbors (KNN), Decision tree, and Partial least squares (PLS) regressions have been chosen to find the best regression types. To find the accuracy, the coefficient of determination (R2), mean absolute error (MAE), and root-mean-square error (RMSE) were selected. Finally, PLS, Linear, and Lasso regressions had better results than other regressions, with R2 greater than 93%, 92%, and 92%, respectively. In general, the present study shows that HPC and UHPC have different mix designs and mechanical properties. In addition, PLS, Linear, and Lasso regressions are the best regressions for predicting mechanical properties.

Equilibrium curves and modeling of binary systems for the carbon di-oxide + benzyl acetoacetate and carbon di-oxide + benzyl acetate mixtures under high pressure

The solution phase behavior of the binary systems of supercritical carbon di-oxide (SU-CO2) + benzyl acetoacetate and SU-CO2 + benzyl acetate was investigated in a synthetic high-pressure apparatus at five temperatures from 313.2 to 393.2 K and pressure up to 33.53 MPa for the industrial benefit of food, pharmaceutical and cosmetics application. The solubility of benzyl acetoacetate and benzyl acetate in the SU-CO2 + benzyl acetoacetate and SU-CO2 + benzyl acetate systems were increased with increasing temperature at constant pressure, respectively. Both system isotherms were exhibited in the simple Type-I category phase behavior. Besides, the Peng-Robinson equation of state has been successfully applied to predict the phase behavior of the SU-CO2 + benzyl ester systems using adjustable molecular interaction parameters (kij and ηij). Neither system shows a three-phase behavior at any point of temperature and pressure. A one-fluid-phase locale was ascertained above and throughout the solubility curve whereas a two-phase locale was exhibited inside the critical curve for both binary systems. The critical mixture curve provides the fingerprint for the phase behavior study of any binary system since it is used to understand and calculate thermodynamic properties effectively. The accuracy of the studied model was tested by evaluating the percentage of root mean square deviation utilizing optimized temperature-dependent mixture parameters. Indeed, this is the first reference point for the prediction of phase transition behavior for benzyl acetoacetate and benzyl acetate in SU-CO2 and the findings make a remarkable impression on industrial applications.

Trade-offs in biomethane production by substrate mixture optimization under German market conditions

BackgroundNew regulations and market conditions in Germany affect the profitability of biomethane upgrading as a repowering option for existing biogas plants following on-site CHP utilization. These conditions present trade-off challenges between higher sustainability requirements, maintaining production capacity and new revenue opportunities. Optimization methods, such as linear programming (LP), are essential for determining the ideal substrate mixture and profitable solutions amidst multiple market conditions, plant-specific process constraints, and substrate properties.MethodsWe updated a substrate mixture optimization model within an assessment framework for the repowering of existing biogas plants (BGPs), which focuses on the operator’s perspective. By integrating multiple German biomethane markets for various BGPs, we assessed changes in the substrate mixture, GHG emissions, contribution margins, and constraint parameters to derive conclusions for operators and future framework design.ResultsIntegrating market revenues and constraints can increase contribution margins by 12–55%. Additional gains can be achieved by considering multiple markets simultaneously but limited to a few BGPs. The plant-specific LP solution space and used benchmark market are decisive. The former limits the potential of high substrate-specific contribution margins, which has a significantly higher impact than the relation between plant-specific characteristics and process constraints. The advanced fuel market is currently the lead market for biomethane, incentivizing GHG-emission extensive substrates, decreasing gas production and GHG emissions but increasing levelized cost of energy (LCOE) and partially CO2 abatement costs.ConclusionsThe key to improve profitability and to supply an increasing biomethane demand while fulfilling new requirements is a large LP solution space. Increasing market options, substrate availability, and digestion system capacity achieve this on the operator’s side. Policy makers could reduce normative requirements such as the maize cap or double counting of advanced fuels and favor high but uniform GHG requirements. Operators can prepare robustly for the future substrate mixture by adding digester volume and pre-treatment tech, ensuring long-term and diverse substrate availability, and contracts with flexible components. Although current market conditions can improve specific GHG emissions, they do not necessarily increase manure usage when other options, such as straw, are viable. Other regulatory support systems will be required to do so.

A model of task-level human stepping regulation yields semistable walking.

A simple lateral dynamic walker, with swing leg dynamics and three adjustable input parameters, is used to study how motor regulation affects frontal plane stepping. Motivated by experimental observations and phenomenological models, we imposed task-level multiobjective regulation targeting the walker's optimal lateral foot placement at each step. The regulator prioritizes achieving step width and lateral body position goals to varying degrees by choosing a mixture parameter. Our model thus integrates a lateral mechanical template, which captures fundamental mechanics of frontal-plane walking, with a lateral motor regulation template, an empirically verified model of how humans manipulate lateral foot placements in a goal-directed manner. The model captures experimentally observed stepping fluctuation statistics and demonstrates how linear empirical models of stepping dynamics can emerge from first-principles nonlinear mechanics. We find that task-level regulation gives rise to a goal equivalent manifold in the system's extended state space of mechanical states and inputs, a subset of which contains a continuum of period-1 gaits forming a semistable set: perturbations off of any of its gaits result in transients that return to the set, though typically to different gaits.

Estimation of the interaction parameters between carbon dioxide and an organic solvent by the Peng–Robinson equation of state via an artificial neural network

The equation of state (EoS) is a tool for estimating the thermodynamic and physical properties of compounds, including mixtures, across a range of temperatures and pressures. When dealing with mixtures, a mixing rule is required to calculate the mixture parameters. Mixing rules may involve interaction parameters, such as kij and lij, that correct for differences between components. However, obtaining this data requires specialized equipment and techniques and significant measurement time, resulting in limited reported EoS parameters. In this study, we introduce an artificial neural network (ANN) to predict interaction parameters in the van der Waals one-fluid mixing rule. These parameters are used to calculate the physical properties of mixtures using the Peng–Robinson (PR) EoS. The interaction parameters are used in two cases, namely the one-parameter and two-parameter mixing rules (OP and TP, respectively), in which only kij and both kij and lij are employed, respectively. The vapor–liquid equilibrium (VLE) data of CO2/organic solvent binary systems are collected and correlated by the PR EoS to construct a database of 1286 and 1292 parameters for the OP and TP, respectively. The molecular weight, critical temperature and pressure, acentric factor of the organic solvent, and temperature are used as input parameters for the ANN. In addition, we optimize the structure of the ANN by changing the activation function, number of neurons, and number of hidden layers. The optimized ANN uses a tanh activation function. Hidden layers are used for both the OP and TP, along with 40 and 50 neurons, respectively. The results confirm that the model can determine the interaction parameters of the PR EoS, which can be used to estimate the VLE. These results are useful for incorporation into process simulators for chemical process design.

Hubungan Antara Rasio Agregat dan Volumetrik dalam Menentukan Kualitas Perkerasan Jalan

Road pavement quality is a critical factor in supporting a country's mobility and economy. Around 40% of the road length in Indonesia suffers damage, including permanent deformation and early cracking. To overcome road damage, it is important to know the pavement quality parameters such as Voids in Mineral Aggregate (VMA), Voids in Mix (VIM), and Voids Filled with Bitumen (VFB), along with the factors that influence them, such as material characteristics. This study aims to determine the effect of the fine and coarse aggregate ratio on volumetric parameters in asphalt mixtures, including VIM, VMA, and VFB. The experimental method was carried out on four samples. Aggregate characteristic tests were carried out, including gradation tests. Volumetric tests were carried out on each sample with asphalt content of 4%, 4.5%, and 5%. The research results show that the smaller fine-to-coarse aggregate ratio would increase the VMA and VIM values and decrease the VFB value. Aggregate ratio significantly affects volumetric parameters, which shows the importance of selecting aggregate with the right size distribution and determining the right ratio to achieve optimal pavement performance. The results of this study can be used as a practical guide for optimizing asphalt mix design with better quality and durability.

Estimation in a general mixture of Markov jump processes

AbstractWe propose a general mixture of Markov jump processes. The key novel feature of the proposed mixture is that the generator matrices of the Markov processes comprising the mixture are entirely unconstrained. The Markov processes are mixed with distributions that depend on the initial state of the mixture process. The maximum likelihood (ML) estimates of the mixture's parameters are obtained from continuous realizations of the mixture process and their standard errors from an explicit form of the observed Fisher information matrix, which simplifies the Louis (Journal of the Royal Statistical Society Series B, 44:226–233, 1982) general formula for the same matrix. The asymptotic properties of the ML estimators are also derived. A simulation study verifies the estimates' accuracy. The proposed mixture provides an exploratory tool for identifying the homogeneous subpopulations in a heterogeneous population. This is illustrated with an application to a medical dataset.

Impact of aggregate gradation and asphalt-aggregate ratio on pavement performance during construction using back propagation neural network

This paper evaluates the influences of variations in asphalt mixture parameters during construction on the variations of pavement performance using back-propagation (BP) neural networks. The variations of gradation (VG) and asphalt-aggregate ratio (VRa) were assessed through a variability analysis. The influences of VG and VRa propagation were analyzed via BP neural networks and a sensitivity analysis. A reliability assessment was conducted to evaluate the joint effects of VG and VRa. Results illustrate that the VG and VRa during transportation are more severe than those during other processes. BP neural networks can precisely and robustly trace the influences of the VG and VRa. Pavement performance exhibits greater sensitivity to VRa and VG at sieve sizes of 0.075 mm and 2.36 mm. The joint effects of VG and VRa significantly degrade permanent deformation more than fatigue life. High-quality paving effectively mitigates the negative impacts of segregation during transportation.