炼焦生产过程质量产量能耗的集成优化控制

针对炼焦生产过程强非线性、大时滞等特点,基于过程参数的主元分析和灰色关联分析,建立了焦炭质量、产量及焦炉能耗的神经网络预测模型和以焦炭质量为约束条件,产量、能耗为目标函数的优化控制模型。提出一种融合模糊C均值聚类粗优化和差分进化细优化的集成优化控制方法,进行过程参数的优化并给出操作优化指导。系统仿真结果表明,该方法能有效地抑制工况的波动,达到高产、优质、低耗的生产目标,为复杂工业过程的建模和优化控制提供了一种新思路。     

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.     

PREDICTION OF QUALITY CHANGES DURING OSMO-CONVECTIVE DRYING OF BLUEBERRIES USING NEURAL NETWORK MODELS FOR PROCESS OPTIMIZATION

Artificial neural network (ANN) models were used for predicting quality changes during osmo-convective drying of blueberries for process optimization. Osmotic drying usually involves treatment of fruits in an osmotic solution of predetermined concentration, temperature and time, and generally affects several associated quality factors such as color, texture, rehydration ratio as well as the finish drying time in a subsequent drier (usually air drying). Multi-layer neural network models with 3 inputs (concentration, osmotic temperature and contact time) were developed to predict 5 outputs: air drying time, color, texture, and rehydration ratio as well as a defined comprehensive index. The optimal configuration of neural network model was obtained by varying the main parameters of ANN: transfer function, learning rule, number of neurons and layers, and learning runs. The predictability of ANN models was compared with that of multiple regression models, confirming that ANN models had much better performance than conventional mathematical models. The prediction matrices and corresponding response curves for main processing properties under various osmotic dehydration conditions were used for searching the optimal processing conditions. The results indicated that it is feasible to use ANN for prediction and optimization of osmo-convective drying for blueberries.     

PREDICTION OF QUALITY CHANGES DURING OSMO-CONVECTIVE DRYING OF BLUEBERRIES USING NEURAL NETWORK MODELS FOR PROCESS OPTIMIZATION

Artificial neural network (ANN) models were used for predicting quality changes during osmo-convective drying of blueberries for process optimization. Osmotic drying usually involves treatment of fruits in an osmotic solution of predetermined concentration, temperature and time, and generally affects several associated quality factors such as color, texture, rehydration ratio as well as the finish drying time in a subsequent drier (usually air drying). Multi-layer neural network models with 3 inputs (concentration, osmotic temperature and contact time) were developed to predict 5 outputs: air drying time, color, texture, and rehydration ratio as well as a defined comprehensive index. The optimal configuration of neural network model was obtained by varying the main parameters of ANN: transfer function, learning rule, number of neurons and layers, and learning runs. The predictability of ANN models was compared with that of multiple regression models, confirming that ANN models had much better performance than conventional mathematical models. The prediction matrices and corresponding response curves for main processing properties under various osmotic dehydration conditions were used for searching the optimal processing conditions. The results indicated that it is feasible to use ANN for prediction and optimization of osmo-convective drying for blueberries.     

单机多产品注塑过程的节能调度方法

为减小单机多产品注塑过程的生产总能耗,提出一种基于旅行商算法(TSP)和遗传算法(GA)的节能调度方法。研究了注塑生产总能耗的3个重要组成:产品切换能耗、过渡调整能耗和稳定生产能耗,建立了产品切换过渡的能耗模型。以单产平稳模态为节点、过渡模态为支路,建立了单机多产品过程生产总能耗的有向图模型,将单机多产品能耗优化问题转化为经典的TSP问题。采用基于遗传算法的多目标逐层优化与TSP路径寻优思想,搜索各个单产平稳生产下的最优操作参数以及多产品的最优生产顺序,以期降低生产总能耗。该方法可提高生产效率,降低生产能耗。应用研究结果验证了方法的可行性和有效性。     

Improving Adsorption Dryer Energy Efficiency by Simultaneous Optimization and Heat Integration

Conventionally, energy-saving techniques in drying technology are sequential in nature. First, the dryer is optimized without heat recovery and then, based on the obtained process conditions, heat recovery possibilities are explored. This work presents a methodology for energy-efficient adsorption dryer design that considers sensible and latent heat recovery as an integral part of drying system design. A one-step pinch-based optimization problem is formulated to determine the operating conditions for optimal energy performance of such an integrated system subject to product quality. Because the inlet and target stream properties of the heat recovery network are determined by the adsorption drying conditions, they are unknown a priori and thus are determined simultaneously within the overall optimization using the pinch location method. Energy balances are written above and below the various pinch point possibilities and the optimal pinch point is that which minimizes the amount of external heating utility required while satisfying drying and thermodynamic constraints. Results for a single-stage zeolite adsorption drying process with simultaneous heat recovery optimization show a 15% improvement in efficiency compared to a sequentially optimized system. The improvement is traceable to alterations in enthalpy-related variables like temperatures and flow rates. The discrepancy in optimal operating conditions between the sequential and simultaneous cases underscores the need to change system operating conditions when retrofitting for heat recovery because previous optimal conditions become suboptimal when heat recovery is introduced. Also, compared to a conventional dryer (without an adsorption process) operating under similar conditions, energy consumption is reduced by about 55%.     

Optimal control of convective drying of saturated porous materials

Numerical simulations of optimal control applied to saturated capillary‐porous materials subjected to convective drying are presented. The optimization process is concerned with such drying parameters as drying rate, energy consumption, and product quality. The thermo‐hydro‐mechanical model of drying is developed to describe the kinetics of drying and to determine the drying‐induced stresses which are responsible for damage of dried products. The effective and the admissible stresses are defined and used to formulate the Huber‐von Mises–Hencky strength criterion enabling assessment of possible material damage. The method of genetic algorithm is used for operation with drying conditions in such a way as to ensure minimum energy consumption and to get the effective stress less than the strength of dried material, and thus, to preserve a good quality of dried products at possibly high drying rate. Numerically simulated optimal drying processes are illustrated on the examples of finite dimensions of kaolin‐clay cylinders subjected to convective drying. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4846–4857, 2013     

Enthalpy-Driven Optimization of Intermittent Drying

Several optimization problems pertinent to intermittent drying of biological materials were analyzed and a tool to resolve each optimization problem was developed. As the product quality is related to the particularities of the thermal treatment, an average enthalpy gain of the product weighted by the maximum enthalpy gain was considered as the objective function to be minimized. A diffusion model was used to estimate the objective function, and process simulations were performed for batch drying of grains in the rotating jet spouted bed. The results show that the optimization of intermittent drying improves considerably the energy performance of such a drying process. Moreover, it offers better product quality due to lower enthalpy gain.     

Enthalpy-Driven Optimization of Intermittent Drying

Several optimization problems pertinent to intermittent drying of biological materials were analyzed and a tool to resolve each optimization problem was developed. As the product quality is related to the particularities of the thermal treatment, an average enthalpy gain of the product weighted by the maximum enthalpy gain was considered as the objective function to be minimized. A diffusion model was used to estimate the objective function, and process simulations were performed for batch drying of grains in the rotating jet spouted bed. The results show that the optimization of intermittent drying improves considerably the energy performance of such a drying process. Moreover, it offers better product quality due to lower enthalpy gain.     

A Preliminary Study on Strategies for Optimal Fluid-Bed Drying

ABSTRACT

The degradation of product quality during convective drying depends on the temperature and water concentration history of the panicles and drying time. For improving product quality in combination with acceptable operation costs, optimal control of the operation variables is required.

In this preliminary study the relevance of dynamic optimal operations for batch-wise fluid-bed drying is explored by simulation. Optimal trajectories of operation variables were calculated for a pilot-plant installation by using a model which concerned the drying history of the panicles and the product quality (inactivation of biological active components). The applied objective function was based on an economic criterion combining product quality and operation costs.

Although the advances for the chosen pilot-plant application are moderate, in future studies the potentials and relevance of dynamic optimal operations for drying will be quantified for installations on industrial scale.     

A Preliminary Study on Strategies for Optimal Fluid-Bed Drying

The degradation of product quality during convective drying depends on the temperature and water concentration history of the panicles and drying time. For improving product quality in combination with acceptable operation costs, optimal control of the operation variables is required.

In this preliminary study the relevance of dynamic optimal operations for batch-wise fluid-bed drying is explored by simulation. Optimal trajectories of operation variables were calculated for a pilot-plant installation by using a model which concerned the drying history of the panicles and the product quality (inactivation of biological active components). The applied objective function was based on an economic criterion combining product quality and operation costs.

Although the advances for the chosen pilot-plant application are moderate, in future studies the potentials and relevance of dynamic optimal operations for drying will be quantified for installations on industrial scale.     

Expected energy analysis for industrial process planning problem with fuzzy time parameters

Industrial process planning is to make an optimal decision in terms of resource allocation. The planning objective can be to minimize the time required to complete a task, maximize customer satisfaction by completing orders in a timely fashion and minimize the cost required to complete a task. Based on time and energy consumption in an industrial process planning problem, a novel energy analysis method is proposed to solve it. According to different constraints and credibility theory, typical expected value models of energy for it are presented. In addition, a hybrid intelligent optimization algorithm integrating fuzzy simulation, neural network and genetic algorithm is provided for solving the proposed expected value models. Some numerical examples are also given to illustrate the proposed concepts and the effectiveness of the used algorithm.     

Optimization of pulsed electric field pretreatment parameters for preserving the quality of Raphanus sativus

Pulsed electric field (PEF) pretreatment can increase the drying rate of produce, but preserving product quality while minimizing energy consumption and maintaining food quality is a significant challenge. The goal of this study was to determine optimal PEF parameters for pretreatment of Raphanus sativus (radish) prior to the drying process. The effects of pulse intensity, treatment time, and pulse number on the drying rate, vitamin C (Vc) content, and ascorbic acid oxidase activity of R. sativus were characterized. Optimal PEF pulse parameter values were determined through quadratic orthogonal regression tests followed by multi-objective nonlinear optimization. The optimal PEF pulse parameters for pretreatment of R. sativus were: pulse intensity, 1446 V · cm^?1; reaction time, 28 μs; and pulse number, 87. This study provides reference values to guide application of PEF pretreatment in R. sativus processing.     

Modeling Intermittent Drying of Wood under Rapidly Varying Temperature and Humidity Conditions with the Lumped Reaction Engineering Approach (L-REA)

For improving product quality and minimizing energy consumption during drying, intermittent drying is often recommended. The mathematical models that are used to describe intermittent drying are usually transport phenomena based, complex models. In this study, the lumped reaction engineering approach (L-REA) is implemented to model wood drying under rapid periodically changed drying air temperature and humidity with high number of cycles of intermittency. The equilibrium activation energy (ΔE _v,b ), an important parameter for REA approach, is evaluated according to the corresponding drying air temperature and humidity in each drying section. The results of modeling suggest the L-REA works well with the experimental data. The simplicity of the L-REA is obvious and is hoped to be used in an industrial setting more readily. The L-REA can be used for sustainable processing in industries to assist in energy audit and management.     

Dynamics And Assessment of Some Control Strategies of a Simulated Industrial Rotary Dryer

Rotary dryers are widely used for the continuous drying of minerals and chemicals on a large scale. Hot gases are passed parallel to the flowing solid to achieve the desired product moisture content. Because these dryers are energy intensive, it is mandatory to operate them as efficiently as possible to respond to economic pressures. Using a dynamic rotary dryer simulator for mineral concentrate, five control strategies are evaluated and compared. Two control strategies are based on PI controllers and the others use neural network models. Results clearly show that a feedforward action, in conjunction with a PI controller or incorporated within the structure of a neural network model, led to the best performances provided an accurate measurement of the feed moisture content is available.     

Energy Consumption and Product Quality Interactions in Flame Spray Drying

The work presents the comparison of two spray drying techniques: conventional concurrent spray drying and novel spray drying technique; that is, flame spray drying (FSD) in terms of energy consumption and quality of final product. The amount of energy consumed to dry maltodextrin solutions showed that for similar moisture evaporation rate and equivalent operating parameters of the drying process, energy consumption was 5 to 30% lower in the FSD process. FSD produced lower bulk and apparent densities of maltodextrin powder and, due to shorter drying time, lower content of fractured particles in comparison with the classical spray drying process.     

Improvement in the Efficiency and Capacity of Chopped Spring Onion Drying

ABSTRACT

Appropriate strategy for drying chopped spring onion with a batchwise flat bed was investigated. Both experimental and simulated results such as product quality, drying capacity and energy consumption were taken into consideration. For simulation work, equations of drying parameters such as specific heat, equilibrium moisture content and thin layer drying were first developed from the lab-scale experimental results. Then a mathematical model including shrinkage for a batchwix flat bed drying was developed. The model was lested with the results obtained from a food processing plant with an acceptable accuracy. Appropriate drying strategy war then investigated. The approximate conclusion was that the drying should be devided into 3 stages. In the 1^st stage, drying air temperature was 80°C, specific air flow rate was 33.9 m³/min -kg dry matter and drying time was 0.5 h. In the 2nd stage, drying air temperature and drying time were kept unchanged but specific air flow rate was decreased to 13.5 m³/min - kg dry matter. In the final stage, drying air temperature was decreased to 67°C, specific air flow rate was also decreased to 6.8 m³/min - kg dry matter and drying time was approximately 1.7 h. Following the suggested strategy, specific primary energy cornsumption was 6.2 MJ/kg H₂O, drying time was 2.7 h and product quality was maintained. It was proven that energy consumption was approximalcly 70% of that of the present practice in the plant.     

混合多目标骨干粒子群优化算法在污水处理过程优化控制中的应用

通过对污水生化处理过程的分析,选取能耗和罚款最低为优化目标,建立污水生化处理过程多目标优化控制模型。为了提高Pareto最优解集的收敛性和多样性,提出一种基于Pareto支配和分解的混合多目标骨干粒子群优化算法（HBBMOPSO）。该方法采用带自适应惩罚因子的分解方法选取个体引导者,采用Pareto支配和拥挤距离法维护外部档案和选取全局引导者。此外,采用精英学习策略增强粒子跳出局部Pareto前沿的能力。最后,将HBBMOPSO与自组织模糊神经网络预测模型和自组织控制器相结合,实现污水生化处理过程溶解氧和硝态氮设定值的动态寻优、智能决策和底层跟踪控制。利用国际基准仿真平台BSM1进行实验验证,结果表明所提HBBMOPSO方法在保证出水水质参数达标的前提下,能够有效降低污水处理过程的能耗。     

OPTIMIZATION OF OPERATING CONDITIONS IN TUNNEL DRYING OF FOOD

Food drying process in tunnel dryer was modeled from Keey's drying model and experimental drying curve, and optimized in operating conditions consisting of inlet air temperature, air recycle ratio and air flow rate. Radish was chosen as a typical food material to be dried, because it has a typical drying characteristics of food and quality indexes of ascorbic acid destruction and browning in the drying. Stricter quality retention constraint required higher energy consumption in minimizing the objective function of energy consumption under constraints of dried food quality. Optimization results of cocurrent and counter current tunnel drying showed higher inlet air temperature, lower recycle ratio and higher air flow rate with shorter total drying time. Compared with cocurrent operation counter current drying used lower air temperature, lower recycle ratio and lower air flow rate, and appeared to be more efficient in energy usage. Most of consumed energy was analyzed to be used for air heating and then escape from the dryer in form of exhaust air.     

A development of the relation between drying energy savings and thermodynamic irreversibility

A general consistent approach to evaluation of drying energy savings is proposed which is based on the energy (available energy) balance of the arbitra dryer. The concept of the so-called energy saving ability function, s_w is introduced, which determines the savings of available energy (related to the unit mass of the moisture evaporated) obtained when the clearly defined drying process is replaced by the optimal one. The importance of the opt process, minimizing the sum of energy and investment costs, as the reference frame for the saving ability function is stressed and the optimization pro for several drying processes with granular solids is outlined. The relation between reduction of the drying irreversibility, increase of energy utiliza efficiency and available energy savings is determined. The optimum of a clearly defined irreversible drying process for the finite time transition of a and a connection between the process time, apparatus price and the optimal process intensity are pointed out. An increase of the possible energy saving to optimization is revealed in the high-rate drying regimes which proves the rationale of the optimization for intensive short-time drying processes. T and quantitative nature of the approach presented here enables its direct application to arbitrary industrial dryers.