Validation of a Method of Determination of Apparent Diffusivity Versus Composition in Solids

A method is presented for computing the values of apparent diffusivity in solids with respect to the concentration of the diffusing substance (water or sodium chloride). This method does not require any assumption upon the mathematical relationship between diffusivity and concentration. It can be applied to experimental measurements of local concentration versus position within the solid (profiles) with relatively few measurements (circa 10) and a mathematical smoothing of the experimental data by using an artificial neural network model. The method was first validated on simulated data obtained by using a constant diffusivity value and on experimental profiles when the relation between diffusivity and concentration was given. It was then applied to original experimental moisture profiles obtained by putting gelatin gels with different initial moisture contents into contact for up to 14 days. The method was also successfully applied to five sets of experimental moisture and sodium chloride profiles taken from the literature and obtained from different food products. Apparent diffusivities calculated by our method were found in agreement with those obtained by authors using different numerical methods to compute the diffusivity values.     

Rapid Method of Determination of Sorption Isotherms and Water Apparent Diffusivity

In the context of a research on the quality of pasta made from soft wheat, reaction kinetics had to be measured at definite temperatures and water contents. It was chosen to realize such experiments in a pilot dryer. To be able to fix temperature and water content of product for a definite period of time, it was first necessary to determine sorption curves of pasta and a drying model including the values of apparent diffusivity of water at elevated temperatures.

This paper presents a method to meet these objectives. It consists in realizing a sequence of plateaux of air relative humidities of decreasing values, at constant temperature, in the dryer. Air-product equilibrium at the end of each plateau would have been too long to attain, therefore equilibrium product water content was calcultated using an exponential model. Apparent water diffusivity was determined by adjustement of a diffusive model, taking in account that the air humidity change from one plateau to the next one was a linear ramp and not a step-wise change.

The average relative repeatability of equilibrium water Content is 0.1% water/dry basis. There is more dispersion on apparent diffusivity.     

Rapid Method of Determination of Sorption Isotherms and Water Apparent Diffusivity

ABSTRACT

In the context of a research on the quality of pasta made from soft wheat, reaction kinetics had to be measured at definite temperatures and water contents. It was chosen to realize such experiments in a pilot dryer. To be able to fix temperature and water content of product for a definite period of time, it was first necessary to determine sorption curves of pasta and a drying model including the values of apparent diffusivity of water at elevated temperatures.

This paper presents a method to meet these objectives. It consists in realizing a sequence of plateaux of air relative humidities of decreasing values, at constant temperature, in the dryer. Air-product equilibrium at the end of each plateau would have been too long to attain, therefore equilibrium product water content was calcultated using an exponential model. Apparent water diffusivity was determined by adjustement of a diffusive model, taking in account that the air humidity change from one plateau to the next one was a linear ramp and not a step-wise change.

The average relative repeatability of equilibrium water Content is 0.1% water/dry basis. There is more dispersion on apparent diffusivity.     

Control of the Moisture Content of Milk Powder Produced in a Spouted Bed Dryer Using a Grey-Box Inferential Controller

Although the moisture content of dried products is an important variable in industrial dryers, it is often not measured directly for control purposes. Alternative and simpler meters might provide information to be used in a physical-mathematical model to estimate the moisture content. When this procedure is applied to a control strategy, an inferential controller is developed. In this article, a physical-mathematical model was used to infer the moisture content of milk powder produced in a spouted bed dryer. Afterwards, simulations of an inferential proportional-integral controller were carried out using the inlet air heating rate as the manipulated variable. The physical-mathematical model used in the procedure was a hybrid model, which considers mass and energy balances and one term which is estimated by an artificial neural network. The controller parameters (controller gain and integral time) were tuned by trial and error. Even though the procedure was quite simple, it was proven to be effective in yielding a stable closed-loop response for both servo and regulatory control of the (inferred) powder moisture content.     

橡胶中扩散和渗透的测定

介绍了在橡胶中各种物质传递过程的基本原理,以及在各种条件下测定扩散和渗透性能的试验方法。     

淀粉可食餐具中的食品添加剂剂量的测定

随着社会发展,资源的合理利用和保护生态环境已经成为21世纪重要的课题,研究制备替代发泡塑料餐具的绿色环保可降解餐具具有重要意义。以淀粉为原料制备环保型可食用餐具具备一定的使用性能,但综合使用性能并不十分理想。通过添加一些食品级添加剂,能提高淀粉餐具的综合使用性能。用预实验确定影响餐具性能的添加食品添加剂的种类及其范围,(参考GB2760《食品添加剂使用卫生标准》),利用正交试验确定淀粉餐具综合使用性能最佳的添加剂配比。结果表明:当其甲基纤维素钠的添加量为0.75 g/100 g、明胶的添加量为0.9 g/100 g、琼脂的添加量为1.575 g/100 g、甘油的添加量为1.5 g/100 g时淀粉可食餐具具有最佳的使用性能。     

混凝土氯离子结合能力的快速测定法

采用等温吸附法研究了混凝土对氯离子的结合能力,并与自然扩散法结果进行了比较.结果表明,等温吸附法测定的氯离子结合能力,通过试件含水率转换之后,与自然扩散法测定的氯离子结合能力基本一致.因此,等温吸附法能够快速测定混凝土的氯离子结合能力,具有较强的实用性.     

Artificial Neural Network Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits

Artificial neural network (ANN) models were developed for the prediction of transient moisture loss (ML) and solid gain (SG) in osmotic dehydration of fruits using process kinetics data from the literature. ANN models for ML and SG were developed based on data over a broad range of operating conditions and ten common processing variables: temperature and concentration of osmotic solution, immersion time, initial water and solid content of the fruit, porosity, surface area, characteristic length, solution-to-fruit mass ratio, and agitation level. The trained models were able to accurately predict the outputs with associated regression coefficients (r) of 0.96 and 0.93, respectively, for ML and SG. These ANN models performed much better than those obtained from linear multivariate regression analysis. The large number of process variables and their wide ranges considered along with their easy implementation in a spreadsheet make them very useful and practical for process design and control.     

Artificial Neural Network Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits

Artificial neural network (ANN) models were developed for the prediction of transient moisture loss (ML) and solid gain (SG) in osmotic dehydration of fruits using process kinetics data from the literature. ANN models for ML and SG were developed based on data over a broad range of operating conditions and ten common processing variables: temperature and concentration of osmotic solution, immersion time, initial water and solid content of the fruit, porosity, surface area, characteristic length, solution-to–fruit mass ratio, and agitation level. The trained models were able to accurately predict the outputs with associated regression coefficients (r) of 0.96 and 0.93, respectively, for ML and SG. These ANN models performed much better than those obtained from linear multivariate regression analysis. The large number of process variables and their wide ranges considered along with their easy implementation in a spreadsheet make them very useful and practical for process design and control.     

Modeling and optimization of photocatalytic/photoassisted-electro-Fenton like degradation of phenol using a neural network coupled with genetic algorithm

Oxidation of phenol in aqueous media using supported TiO₂ nanoparticles coupled with photoelectro-Fenton-like process using Mn²⁺ cations as catalyst of electro-Fenton reaction was studied. The influence of the basic operational parameters such as initial pH of the solution, applied current, initial Mn²⁺ concentration, initial phenol concentration and kind of ultraviolet (UV) light on the oxidizing efficiency of phenol was studied. An artificial neural network (ANN) model was coupled with genetic algorithm to predict phenol oxidizing efficiency and to find an optimal condition for maximum phenol removal. The findings indicated that ANN provided reasonable predictive performance (R² = 0.949).     

共振散射光谱分析法测定纯净水中氯化物

在硝酸和Triton X-100余质中,氯化物能与硝酸银定量反应生成稳定的氯化银纳米微粒,存在共振散射效应。Cl^-的浓度在0．071～5．325μg／mL范围内与共振散射强度（IRS）成较好的线性关系。检出限为0．034μg／mL,用于纯净水中氯化物含量的测定,具有简单、快速、灵敏度高等特点,结果满意。     

An Investigation of Drying Process of Shelled Pistachios in a Newly Designed Fixed Bed Dryer System by Using Artificial Neural Network

In this paper, the drying of Siirt pistachios (SSPs) in a newly designed fixed bed dryer system and the prediction of drying characteristics using artificial neural network (ANN) are presented. Drying characteristics of SSPs with initial moisture content (MC) of 42.3% dry basis (db) were studied at different air temperatures (60, 80, and 100 °C) and air velocities (0.065, 0.1, and 0.13 m/s) in a newly designed fixed bed dryer system. Obtained results of experiments were used for ANN modeling and compared with experimental data. Falling rate period was observed during all the drying experiments; constant rate period was not observed. Furthermore, in the presented study, the application of ANN for predicting the drying time (DT) for a good quality product (output parameter for ANN modeling) was investigated. In order to train the ANN, experimental measurements were used as training data and test data. The back propagation learning algorithm with two different variants, so-called Levenberg–Marguardt (LM) and scaled conjugate gradient (SCG), and tangent sigmoid transfer function were used in the network so that the best approach can be determined. The most suitable algorithm and neuron number in the hidden layer are found out as LM with 15 neurons. For this number level, after the training, it is found that Root-mean squared (RMS) value is 0.3692, and absolute fraction of variance (R²) value is 99.99%. It is concluded that ANNs can be used for prediction of drying SSPs as an accurate method in similar systems.     

Artificial neural network model to predict slag viscosity over a broad range of temperatures and slag compositions

Threshold slag viscosity heuristics are often used for the initial assessment of coal gasification projects. Slag viscosity predictions are also required for advanced combustion and gasification models. Due to unsatisfactory performance of theoretical equations, an artificial neural network model was developed to predict slag viscosity over a broad range of temperatures and slag compositions. This model outperforms other slag viscosity models, resulting in an average error factor of 5.05 which is lower than the best obtained with other available models. Genesee coal ash viscosity predictions were made to investigate the effect of adding Canadian limestone and dolomite. The results indicate that magnesium in the fluxing agent provides a greater viscosity reduction than calcium for the threshold slag tapping temperature range.     

气固循环流化床颗粒浓度波动信号的预测

结合重构相空间方法与人工神经网络法，建立了混沌时间序列预测模型. 应用此模型对f100 mm?16 m的上行气固循环流化床系统中的FCC固体颗粒局部颗粒浓度波动信号进行了预测. 结果表明：循环流化床的颗粒浓度波动信号只能被短期预测，其长期行为是不可预测的，这从另一个角度说明气固循环流化床系统是一混沌系统.     

Sensitivity and Optimization of Peel Strength Based on Composition of Adhesives for Footwear Industry

In order to contribute to the research and development of adhesives for the footwear industry, this paper aims to develop a model capable to predict and optimize the peel strength from the composition of adhesives. The proposed approach is based on three stages: experimental planning of measurements, global sensitivity analysis for uncertainty propagation, and optimization procedure. The design variables are the weight percentages of the solid raw material constituents such as polyurethane, resins, and additives of the adhesive joint. Considering the experimental results obtained for Taguchi design points as input/output patterns, an artificial neural network (ANN) is developed based on supervised evolutionary learning. Using the developed ANN a global sensitivity analysis procedure is implemented and the variability of the structural response of adhesive joint is studied. The optimal solution for adhesives composition for maximum peel strength is investigated based on ANN model and using a genetic algorithm. The proposed approach is able to predict the optimal peel strength including its sensitivity to uncertainties. The results show that the sensitivities of design variables belonging to polyurethane and additive groups are important for optimal adhesive joint. The optimal peel strength based on proposed approach is consistent with the experimental testing data.     

加巴喷汀原料中残留溶剂测定方法验证

加巴喷汀是美国Warner-Lanbert公司首先开发的抗癫痫药,是γ-氨基丁酸（GABA）的衍生物,于1993年首次在英国上市。其生产中的残留溶媒包括二氯甲烷、甲醇、乙醇、乙酸乙酯和异丙醇应进行严格控制。使用气相色谱法,采用顶空进样,参照ICH中Q3C对残留溶剂的要求,对加巴喷汀中的残留溶媒分析方法进行验证。验证结果表明,本方法精密度高,线性良好,能够满足药品质量控制的需要。     

ANN Analysis of a Roller Compaction Process in the Pharmaceutical Industry

Dry granulation via a roller compactor was simulated based on the artificial neural network (ANN) methodology. Two process parameters, including roll force and screw speed, were considered as input of the simulation whereas ribbon density was considered as output. Experimental work was carried out using an industrial‐scale roller compactor. The results showed an excellent agreement between simulation and experiments. The findings were compared as well with the results obtained in a previous study employing the Johanson model, which is the predominant model for the simulation of a roller compaction process. The overall deviation observed for the developed ANN model was found to be significantly improved in comparison with the deviation obtained for the Johanson model. The results demonstrated a very good capability and robustness of the developed ANN model in design and optimization of the roller compaction process.     

Quantification and Determination of Composition of Steryl Ferulates in Refined Rice Bran Oils Using an UPLC-MS Method

Gamma-oryzanol contains a mixture of steryl ferulates found in rice bran oil. Several studies have attributed nutraceutical properties to this mixture, such as hypocholesterolemic and anti-inflammatory activities. A method based on ultra-performance liquid chromatography coupled with electrospray ionization mass spectrometry was developed and evaluated for the simultaneous quantification of gamma-oryzanol and identification of five major steryl ferulates directly in refined rice bran oils (RBO) samples. The proposed method was evaluated according to linearity by obtaining standard curves with R ² values above 0.990, and limit of detection values ranged from 1.9 to 5.9 µg/mL, whereas limits of quantification ranged from 5.9 to 17.9 µg/mL; inter- and intraday accuracy and precision were within the range required by the US Food and Drug Administration guidelines; recovery levels ranged from 78 to 85% for gamma-oryzanol, and from 84 to 119% for steryl ferulates. The method can be considered robust in relation to the NH₄OH (ammonium hydroxide) content and cone voltage variations, with coefficient of variation and average relative percentage deviation values lower than 7.0 and 4.4%, respectively. The stability during the storage test was maintained in concentrated samples (18.5 µg/mL), with recovered values between 93 and 113%. This method was successfully applied to the analysis of RBO samples, demonstrating that it could be easily used for quality control purposes.     

Ozonation Kinetics of Acid Red 27 Azo Dye: A Novel Methodology Based on Artificial Neural Networks for the Determination of Dynamic Kinetic Constants in Bubble Column Reactors

A procedure for the determination of initial parameter values for quadratically convergent optimization methods is proposed using artificial neural networks coupled with a non-stationary gas-liquid reaction model. The evaluation of the regression and the mean squared error coefficients of the neural network during its training process allow the parameter sensitivity analysis of the gas-liquid model. This analysis examines how many and which parameters of the model will be available depending on the observable information of the mathematical model. Numerical simulations show the relevance of the initial values and the non-linearity of the objective function. The methodology has been applied to the study of the reaction of the azo-dye Acid Red 27 with ozone in acid media. The rate constant is in the order of (1.6 ± 0.1) 10³ M^?1 s^?1 under the experimental conditions.