流化床中生物质热解过程的混沌神经网络模拟

在流化床内用若干种生物质材料进行了氮气流化条件下的常压热解实验.为研究生物质的热解规律,建立了混沌神经网络模型对其进行模拟.分别按照3种方案进行了神经网络的模拟,经过比较确定了对于流化床内生物质热解过程最为有效的网络输入方案,该方案充分考虑了实验运行参数、生物质料的工业分析数据以及化学成分分析数据,可以对热解产物给出较好的预测.最后基于生物质与煤在形成过程与化学结构等方面的不同之处对上述方案间的差异给出了合理的解释.     

复式干燥装置

颗粒以流通的流化床作初步干燥再以旋风分离室作精干燥的复式干燥装置(以下简称旋风型干燥器),经实验室模型的试验表明,没有喷动床干燥器和流化床干燥器所有的缺点。物料在流通的流化床设备中初步干燥和普通的流化状态不同:在干燥器的下部主要是流化状态,而上部为气流输送状态。产品的精干燥在旋风干燥室中进行。旋风干燥室是一个气相悬浮系自切向引入的锥形干燥器。另外还有一条管道系统(图上设备1～4)接至旋风干燥室,供给补充气流。与喷动床的不同点在于旋风干燥室的各段上空气速度远大于颗粒旋扬速度。旋风干燥室的锥度是干燥过程强化的     

流化床干燥器运行过程中影响流化的因素分析

在化学工业中,湿分是以自由溶剂和（或）化合剂溶剂存于湿物料中的水分或溶剂。我们常常把借热能使湿物料中湿分气化,并将产生的蒸汽由惰性气体带走,从而得到含有一定含有湿分的固体产品的过程叫做干燥。而干燥由恒速干燥和降速干燥两个过程组成,它们影响着干燥速率的好坏。在正常的生产中,干燥器的应用非常广泛。但结块现象的出现严重的影响了车间的正常生产与产品的质量,因此流化行为的正常与否已成为干燥器运行过程好坏的重要指标。流化干燥是指干燥介质使固体颗粒在流化状态下进行干燥的过程。本文就以振动流化床为例,从流化床干燥器的运行过程出发。对流化床干燥器的工作原理、影响流化的因素及其解决方案进行分析,以期望提升流化率,提高生产效率。     

基于气固流态化原理的油页岩干燥动力学

为了考察气固流化床干燥器能否使油页岩含水质量分数达到要求,以柳树河油页岩颗粒为原料,研究进口气体温度和颗粒直径对油页岩干燥性能的影响,采用薄层干燥模型,对油页岩干燥实验数据进行模拟,确定油页岩干燥方程和干燥速率方程,建立油页岩干燥速率特征常数和有效扩散系数之间的关联式。研究结果表明:薄层干燥模型中修正Page模型Ⅰ适合描述油页岩的干燥过程;油页岩在流化床内干燥过程主要发生在降速干燥阶段,进口气体温度越高,油页岩颗粒直径越小,所需干燥时间越短,进口气体温度为350℃时,使2.4 mm油页岩含水质量分数低于5%,所需干燥时间为2.5 min。     

流化床干燥己二酸影响因素的探讨

辽阳石化公司新己二酸装置中的干燥工序采用的是流化床干燥技术。通过实际生产的探索,对影响流化床干燥器运行的因素进行研究,揭示流化床干燥技术的特点,并为该装置流化床干燥器在目前操作情况下提出了优化方向。     

流化床式生物质热解过程模拟研究进展

生物质能作为一种可再生的清洁能源,受到国内外研究者越来越多的关注。流化床式生物质热解转化技术是清洁高效转化利用生物质的新方法。本文在分析流化床式生物质热解过程的特征结构基础上,着重对各类热解流化床模拟进展进行了归纳总结。并提出了采用多尺度建模方法来提高生物质热解模拟的计算速度和精度,表明该类模型具有指导生物质热解过程优化与放大的应用潜力。     

褐煤脉冲式气流干燥传递过程研究

褐煤干燥是褐煤固体热载体新法干馏工艺的重要组成部分。本文对脉冲式气流干燥器内基于气-固两相流的褐煤干燥动量、热量、质量传递过程进行了研究。依托高效成熟的气流干燥技术,以颗粒在气流中的受力情况为基础,建立了气流干燥过程中颗粒加速运动的热量传递模型和颗粒减速运动的动量及热量传递模型,并提出了新的脉冲式气流干燥器高度的设计及优化方法。通过将脉冲式气流干燥器的干燥模拟数据与相同高度的等径式气流干燥器的数据进行对比,验证了脉冲式气流干燥的高效性,并根据烟气温度、颗粒湿含量、体积传热系数等模拟参数沿干燥器高度的分布情况对其高效性的原因进行了详细分析,为脉冲式气流干燥装置工艺设计提供准确的数据依据和理论参考。     

湿污泥颗粒的流化床干燥实验及模型

在鼓泡流化床内以河砂为干燥介质,对单颗粒湿污泥的流态化干燥特性进行实验研究,得到了流化床温度、污泥初始水分、污泥粒径及流化速度对干燥速率的影响规律：流化床温度及污泥粒径对干燥速率的影响都呈指数规律;污泥的水分越大,干燥速率越大;在鼓泡流化床流化速度达到2倍临界流化速度以上时,充分流化,流化速度再增大(2~5倍临界流化速度)对干燥速率没有明显影响. 在基本的扩散传质理论的基础上,利用实验数据回归得到湿污泥在鼓泡流化床内干燥的半经验模型,为流化床污泥干燥器的设计提供了基础数据和依据.     

双流化床生物质气化的三维全循环数值模拟

基于多相质点网格方法 (multi-phase particle-in-cell,MP-PIC)对工业尺度的双流化床生物质气化过程进行了三维全循环数值模拟。其中,在拉格朗日框架下求解颗粒团运动,采用大涡模拟法(large-eddy simulation, LES)求解气相湍流,同时考虑复杂的气固耦合以及生物质的热解、气化、均相/异相反应。首先,通过独立性检验确定了计算所需的最佳网格数与计算颗粒数,且模拟结果和实验结果对比良好。其次,揭示了流化床内生物质气化过程中的气固流动特性及气体组分分布规律,研究了床内温度、生物质粒径、曳力模型等因素对产物气体组分分布的影响。结果表明:温度升高,出口处的CO摩尔分数增加,而其余组分都减小;较小生物质粒径的气化效果要优于较大的生物质颗粒粒径;曳力模型对各产物气体组分的摩尔分数几乎无影响。     

组合式干燥系统的分段优化设计方法

提出了分离过程的分段设计法：将一个任务分解为多个子任务,根据各个子任务的不同特点进行设计. 对干燥过程进行优化设计,以年费用最小为目标,将干燥过程表示为最多3段的超结构,每段有2个干燥设备可以选择,给出了单元和过程系统的模型和经济模型. 实例研究结果表明,当物料含水量大于物料在该条件下的临界含水量时,宜采用两段干燥的方法,将回转圆筒干燥器和流化床干燥器组合进行干燥;当物料含水量低于临界含水量时,宜采用一段干燥,将物料直接放入流化床干燥器进行干燥.     

Innovative Steam Drying of Empty Fruit Bunch with High Energy Efficiency

Empty fruit bunch (EFB) is one of the solid wastes from crude palm oil mills and has the lowest value for utilization compared to other solid wastes. To achieve an efficient utilization of EFB, drying is considered the first crucial process due to the high moisture content of EFB. In this study, EFB drying based on exergy recovery is proposed to achieve high energy efficiency. A fluidized bed is adopted as the main dryer. The proposed model is evaluated in terms of energy efficiency, especially regarding the influence of target moisture content and fluidization velocity. Up to 92% of the energy involved in the drying process can be recirculated. The total energy consumption for drying decreases as the target moisture content decreases, though there is no significant impact of fluidization velocity to total energy consumption. In addition, the required total length of the heat transfer tubes immersed inside the fluidized bed dryer is calculated because it relates to fluidization performance and economic issues. Lower target moisture content results in a longer heat transfer tube, and higher fluidization velocity leads to a shorter heat transfer tube.     

Continuous fluidized bed drying: Residence time distribution characterization and effluent moisture content prediction

The mixing and drying behavior in a continuous fluidized bed dryer were investigated experimentally by characterizing the residence time distribution (RTD) and incorporating a micromixing model together with the drying kinetics obtained from batch drying. The RTD of the dryer was modeled using a tank-in-series model. It was found that a high initial material loading and a low material flow rate resulted in a reduced peak height and broaded peak width of the RTD curve. To predict the continuous dryer effluent moisture content, we combined: (a) the drying kinetics as determined in a batch fluidized bed dryer, (b) the RTD model, and (c) micromixing models—segregation and maximum mixedness models. It was found that the segregation model overpredicted the effluent moisture content by up to 5% for the cases we have studied while the maximum mixedness model gave a good prediction of the effluent moisture content.     

Evaluation of the air-borne ultrasound on fluidized bed drying of shelled corn: Effectiveness,grain quality,and energy consumption

The objective of the study was to investigate the influence of high power ultrasound on a laboratory-scale fluidized bed shelled corn dryer. The drying time, moisture content variation, specific energy consumption, and quality parameters including ultimate compressive strength, toughness, shrinkage and color of corn kernels were investigated. Furthermore, artificial neural network (ANN) simulation models were developed for predicting the drying variables. Machine vision techniques were used to determine color and shrinkage as qualitative indices. Results showed that the lower frequencies had better penetrations at lower temperatures and cause a significant reduction in drying time. In addition, the ultrasound application led to reduction of ultimate compressive strength and toughness of the dried samples although ultrasound has nonthermal character as the subsidiary factor, it plays an important role in shrinkage and color specification. Based on error analysis results, the prediction capability of ANN model is found to be reasonable for the developed models. Application of ultrasound significantly decreased the specific energy consumption of drying process at the optimal drying condition.     

Experimental and numerical analysis of oil shale drying in fluidized bed

The main objective of this work was to experimentally and numerically investigate the Liu Shu River oil shale drying by the means of flue gas in a fluidized bed dryer. Several experiments were performed under different temperatures conditions. The moisture content of oil shale was measured during the experiments. The two-stage drying model was incorporated in computational fluid dynamics (CFD) package FLUENT via user-defined functions (UDF) and utilized for simulation of heat and mass transfer of oil shale drying in the fluidized bed dryer. The simulation results for solid moisture content agreed well with experimental data. The effects of the temperature and velocity of flue gas, initial bed height, and the particle size on the drying characteristics were predicted and analyzed. It is shown that the gas temperature and velocity are the important parameters in the whole drying process. The particle size has more obvious influence in the falling drying period than the constant drying period. The temperatures of gas and solid phases were monitored. It is shown that the so-called “near gas distributor zone” is the most effective heat transfer zone, which agrees well with the calculated value. The system quickly reached thermal equilibrium, characterizing a nearly isothermal bed. The developed model provides a very good demonstration to describe the oil shale drying in the fluidized bed dryer, and may provide important information for design, optimization of operation conditions.     

Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using Artificial Neural Network

Drying characteristics of shelled corn (Zea mays L) with an initial moisture content of 26% dry basis (db) was studied in a fluidized bed dryer assisted by microwave heating. Four air temperatures (30, 40, 50 and 60 °C) and five microwave powers (180, 360, 540, 720 and 900 W) were studied. Several experiments were conducted to obtain data for sample moisture content versus drying time. The results showed that increasing the drying air temperature resulted in up to 5% decrease in drying time while in the microwave-assisted fluidized bed system, the drying time decreased dramatically up to 50% at a given and corresponding drying air temperature at each microwave energy level. As a result, addition of microwave energy to the fluidized bed drying is recommended to enhance the drying rate of shelled corn. Furthermore, in the present study, the application of Artificial Neural Network (ANN) for predicting the drying time (output parameter for ANN modeling) was investigated. Microwave power, drying air temperature and grain moisture content were considered as input parameters for the model. An ANN model with 170 neurons was selected for studying the influence of transfer functions and training algorithms. The results revealed that a network with the Tansig (hyperbolic tangent sigmoid) transfer function and trainrp (Resilient back propagation) back propagation algorithm made the most accurate predictions for the shelled corn drying system. The effects of uncertainties in output experimental data and ANN prediction values on root mean square error (RMSE) were studied by introducing small random errors within a range of ±5%.     

CFD Modeling of Microwave-Assisted Fluidized Bed Drying of Moist Particles Using Two-Fluid Model

The drying behavior of moist spherical particles in a microwave-assisted fluidized bed dryer was simulated. The two-fluid Eulerian model incorporating the kinetic theory of granular flow was applied to simulate the gas–solid flow. The simulations were carried out using the commercial computational fluid dynamics (CFD) package Fluent 6.3.26. The effects of different levels of microwave power densities as well as initial gas temperature on the prediction of solids moisture content, gas temperature, and gas absolute humidity were investigated. The effect of microwaves was incorporated into calculations using a concatenated user-defined function (UDF). The simulation results were compared with experimental data obtained from drying of soybeans in a pilot-scale microwave-assisted fluidized bed dryer and reasonable agreement was found. The mean relative deviation for prediction of solids moisture content, gas temperature, and gas absolute humidity were less than 3, 10, and 5%, respectively. Further work is needed to validate the proposed model for large-scale plants.     

Drying of Carrots in a Fluidized Bed. II. Design of a Model Based on a Modular Neural Network Approach

The present paper deals with the design of a neural network type model for drying of carrots, which includes the associated transport mechanisms of the process. The model uses the operational variables and the time as input parameters. Two sub-layers of linear and sigmoidal nodes make up the hidden layer, to represent the external and internal resistances to the diffusion of water vapors during the drying process. The single output node weights the contribution of each mechanism of the drying process to predict the exit moisture content of the product. This model was used to predict the drying of carrot particles in a mechanically fluidized bed dryer reported in a previous paper [Reyes, A.; Alvarez, P.; Marquardt, F. Drying of carrots in a fluidized bed: I.- effects of drying conditions and modeling. Drying Technology 2002, 20 (7), 1463-1483.]. Simulated drying curves obtained with this model fits adequately the curves determined experimentally for the most operation conditions, which would indicate that this model is appropriate to be used for rough estimations in the design, the selection of optimal operational conditions, and the scaling up of dryers.     

An Evaluation of Factors Influencing the Energy-Efficient Operation of Well-Mixed Fluidized Bed Dryers

This article describes the results of calculations of specific energy consumption, E_s, performed on a well-mixed fluidized bed dryer simulator. Exhaust air temperature-humidity loci required to yield a specified outlet moisture content were also determined. Most of the calculations related to solids whose drying rate was gas-film controlled. Six model drying curves were employed to examine the effects of drying rate and hygroscopicity in addition to the normal operating parameters. The results indicated that E_s was highest for slow-drying hygroscopic solids and lowest for fast-drying, non-hygroscopic solids. Specific energy consumption increased with decreasing bed temperature and outlet moisture content and with increasing heat loss but was independent of solids loading and airflow rate. For both the aforementioned solids and a much slower drying material (wheat), there was close agreement between the zero heat loss data and a single theoretical curve approximating the performance of an ideal adiabatic dryer. Distinct differences between the behavior of well-mixed and plug flow fluidized bed dryers are reported.     

Prediction of Moisture Content Evolution in a Grain Batch Dryer Based on On–line Temperature Measurements

ABSTRACT

The time evolution of the moisture content in a grain batch fluidized bed dryer is estimated by means of an on–line non–linear estimator (Marquardt–Levenberg algorithm). The inputs to the estimation algorithm are on–line temperature measurements and the output is the surface moisture content. The surface moisture content is used for predicting the grain moisture content through the solution of a single ordinary differential equation that combines the moisture and energy balances over the dryer. In this way the drying curves are obtained through incorporating in a very simple model easily obtainable physical information of the process.     

Simulation and Validation of Microwave-Assisted Fluidized Bed Drying of Soybeans

A general mathematical model of heat and mass transfer was developed to simulate the microwave-assisted fluidized bed drying of bulk grain. The model was solved using the well-known Runge-Kutta-Gill method. The model is capable of predicting the moisture content of soybean as well as the drying air parameters (i.e., drying air temperature and moisture content) during drying. The values of mean relative deviation (MRD) were less than 8 and 10% for prediction of grain moisture content and outlet air parameters, respectively, which reflects an acceptable accuracy. In comparison with conventional fluidized bed drying of soybean, microwave-assisted fluidized bed drying led to 83.39–98.07% savings in drying time and 82.07–95.22% savings in specific energy consumption when reducing soybean moisture content from 18.32 to 12% (db).