Studies on the mashing conditions of teff (Eragrostis tef) malt as a raw material for lactic acid‐fermented gluten‐free beverage

Formation of extracts and fermentable sugars during mashing can be limited by incomplete starch gelatinisation. The aim of this research was to develop mashing programme for 100% teff malt as a potential raw material for gluten‐free lactic acid‐fermented beverage. Isothermal mashing at temperatures ranging between 60 and 84 °C was conducted, and the highest extract (85%) was observed for the wort samples produced at temperatures higher than 76 °C. Sixty‐minute rest at 71 °C resulted in higher fermentable sugars than other tested conversion rest temperatures. Inclusion of lower mashing‐in temperature in the mashing programme also substantially improved the concentrations of free amino nitrogen (128 mg L^?1) and fermentable sugar (58 g L^?1) in the final wort. Therefore, 30‐min rest at 40 °C followed by 60‐min rest at 71 °C and 10‐min rest at 78 °C was found to be a suitable mashing programme for teff malt.     

淀粉-阳离子接枝共聚荧光乳液的制备及性能

利用亲核取代法制备含有紫外线吸收基团的二苯乙烯型荧光单体（FBs），然后采用接枝共聚技术将FBs、苯乙烯分别与二甲基二烯丙基氯化铵（DMDAAC）和甲基丙烯酰氧乙基三甲基氯化铵（DMC）接枝聚合在淀粉大分子上，制备两种阳离子荧光乳液（ST-DMDAAC-FBs / ST-DMC-FBs）。采用1H-NMR、FT-IR、UV-vis、荧光光谱对产物的结构及光学性能进行表征；通过抗张强度、接触角、紫外光老化试验、SEM等手段对施胶前后纸张的物理性能进行测试。结果表明，经ST-DMDAAC-FBs、ST-DMC-FBs施胶纸张的抗张强度相比空白纸张分别提高43.1%、39.7%，白度分别提高15.52 %ISO、15.28 %ISO，返黄值分别降低1.88、1.75。说明ST-DMDAAC-FBs、ST-DMC-FBs均可以提高纸张白度与强度、抑制纸张返黄，且ST-DMDAAC-FBs对纸张的作用效果更佳。     

Application Of Superconducting High Gradient Magnetic Separation Technology On Silica Extraction From Iron Ore Beneficiation Tailings

Comprehensive utilization of tailings resources not only solves environmental problems but also creates huge economic benefits. In this paper, fine weakly magnetic particles were extracted from iron ore beneficiation tailings and silica (SiO₂) was separated and purified using a superconducting high gradient magnetic separator. Based on plenty of contrast experiments, SiO₂ content of the magnetic concentrate could be increased from 68.67% to 92.57% with the selected optimal parameter combination, i.e., a magnetic flux density of 3.2 T, a pulp concentration of 40 g/L and a slurry flow velocity of 500 mL/min. The concentrate can be utilized as low-grade normal SiO₂ sands for industrial use, or act as the raw materials for preparing high-purity SiO₂ product.     

Optimum surface area distribution of quadruple-effect evaporator in sugar juice evaporation process

The evaporation process in raw sugar manufacturing consists of juice heater, multiple-effect evaporator, and crystallizer. Two performance parameters of the evaporation process are the rate of processed sugar juice and the steam economy, defined as the ratio between water content of sugar juice entering the process to the amount of steam required to run the process. If the total surface area of the multiple-effect evaporator is fixed, the performance parameters are affected by the distribution of the surface area among the effects of the evaporator. Although the optimization of evaporator surface area distribution has been investigated in previous studies, they have considered only the multiple-effect evaporator, and overlooked interactions between the three components of the evaporation process. The current investigation proposes a model of the evaporation process that takes into account interactions between the three components of the process through mass and energy balances. The model is used to find the optimum surface area distribution that maximizes the rate of sugar juice processed by the system and the optimum surface area distribution that maximizes the steam economy are different.     

Cellulosic fines: Properties and effects

Any cellulosic pulp consists of particles of different dimensions. When trying to understand and control its properties, it is important to consider not only the bulk amount of long fibres, but also the material known as ‘fines’, which may comprise between 1 and 40% of a pulp. These fines have a great impact on the behaviour of pulp, on its processing, and on the characteristics of the resulting products. We compiled a review of research efforts to characterise the fines fraction by origin, morphology, and chemical composition, and to evaluate the fines’ effects especially in papermaking. The main feature of fines is the large specific surface area associated with their size. Their chemical constitution, particularly their charge, and the magnitude of their surface are the basis for their interactions with other pulp components such as extractives, fillers, and retention aids. The surface of fines affects drainage, as well as sheet density and strength. Several optical paper properties are influenced by the morphology of fines and by their chemical composition, which deviates from that of the long fibre fractions. The targeted utilisation of fines is a potential control variable in papermaking applications in order to obtain desirable paper properties.     

Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks

The aim of this work was to model and predict the process of bioethanol production from intermediates and byproduct of sugar beet processing by applying artificial neural networks. Prediction of one substrate fermentation by neural networks had the same input variables (fermentation time and starting sugar content) and one output value (ethanol content, yeast cell number or sugar content). Results showed that a good prediction model could be obtained by networks with single hidden layer. The neural network configuration that gave the best prediction for raw or thin juice fermentation was one with 8 neurons in hidden layer for all observed outputs. On the other side, the optimal number of neurons in hidden layer was found to be 9 and 10 for thick juice and molasses, respectively. Further, all substrates data were merged, which led to introducing an additional input (substrate type) and defining all outputs optimal network architecture to 3-12-1. From the results the conclusion was that artificial neural networks are a good prediction tool for the selected network outputs. Also, these predictive capabilities allowed the application of the Garson's equation for estimating the contribution of selected process parameters on the defined outputs with satisfactory accuracy.