PRODUCT QUALITY MULTI-OBJECTIVE OPTIMIZATION OF FLUIDIZED BED DRYERS

ABSTRACT

Design of fluidized bed dryers constitutes a mathematical programming problem involving the evaluation of appropriate structural and operational process variables so that total annual plant cost involved is optimized. The increasing need for dehydrated products of the highest quality, imposes the development of new criteria that, together with cost, determine the design rules for drying processes. Quality of dehydrated products is a complex resultant of properties characterizing the final products, where the most important one is color. Color is determined as a three-parameter resultant, whose values for products undergone drying should deviate from the corresponding ones of natural products, as little as possible. In this case, product quality dryer design is a complex multi-objective optimization problem, involving the color deviation vector as an objective function and as constraints the ones deriving from the process mathematical model. The mathematical model of the dryer was developed and the fundamental color deterioration laws were determined for the drying process. Non-preference multi-criteria optimization methods were used and the Pareto-optimal set of efficient solutions was evaluated. An example covering the drying of sliced potato was included to demonstrate the performance of the design procedure, as well as the effectiveness of the proposed approach.     

PRODUCT QUALITY MULTI-OBJECTIVE DRYER DESIGN

Design of conveyor-belt dryers constitutes a mathematical programming problem involving the evaluation of appropriate structural and operational process variables so that total annual plant cost involved is optimized. The increasing need for dehydrated products of the highest quality, imposes the development of new criteria that, together with cost, determine the design rules for drying processes. Quality of dehydrated products is a complex resultant of properties characterizing the final products, where the most important one is color. Color is determined as a three-parameter resultant, whose values for products undergone drying should deviate from the corresponding ones of natural products, as little as possible. In this case, product quality dryer design is a complex multi-objective optimization problem, involving the color deviation vector as an objective function and as constraints the ones deriving from the process mathematical model. The mathematical model of the dryer was developed and the fundamental color deterioration laws were determined for the drying process. Non-preference multi-criteria optimization methods were used and the Pareto-optimal set of efficient solutions was evaluated. An example covering the drying of sliced potato was included to demonstrate the performance of the design procedure, as well as the effectiveness of the proposed approach.     

THE DESIGN AND OPTIMIZATION OF AN INDUSTRIAL DRYER FOR SULTANA RAISINS

A mathematical model of a tunnel dryer for sultana grapes is presented and applied for the determination of size and optimal operating conditions of the dryer. The optimum condition is given by the minimization of heat consumption, expressed as the ratio of thermal load to production, with some constraints regarding the production rate of the dryer and the maximum permissible air temperature. The optimization variables are temperature and humidity of the drying air, and product loading thickness on the trays. The optimum condition requires the operation of the dryer on the maximum permissible air humidity, which corresponds to a high degree of recirculation of exhaust air. This can be accomplished using automatic control of fresh air and humid air inlet and exhaust dampers along the length of the dryer, during the entire drying cycle.     

THERMOCOMPRESSOR DESIGN FOR EFFICIENT ENERGY RECOVERY OF PAPER PRODUCTION PLANT DRYERS

Abstract

The operational performance of a paper production plant can be significantly improved by appropriate process interventions. This paper deals with energy aspects of the most important section of such a plant, the paper drying section. A significant intervention that would lower the operational cost of the drying process is proposed. It refers to the recompression of low pressure steam leaving the dryers by means of a thermocompressor that would utilize high pressure steam coming from the plant burners. The mathematical model of the thermocompressor is evaluated and its design procedure is formulated as a mathematical programming problem involving appropriate objective function and constraints.     

动态模拟优化尿素产品质量控制

运用动态模拟对尿素装置蒸发系统非正常工况进行分析,找出尿素产品质量参数的变化规律和影响因素,提出操作上的优化措施。     

COMPUTER ANALYSIS OF INDUSTRIAL BATCH DRYER AERODYNAMICS

ABSTRACT

One of the fundamental problems encountered in the batch dryer design field is the determination of appropriate equipment configuration that would ensure uniform distribution of air over the dryer trays. Such industrial batch dryer aerodynamics problems can be successfully investigated using computational fluid dynamics techniques. A mathematical model for predicting the two-dimensional air flow inside a typical industrial batch dryer equipment is developed and analyzed. The model consists of the full set of partial differential equations that describe the conservation of mass and momentum inside the dryer. The standard k-E model is used to describe turbulence in addition to the governing conservation equations. Distribution of drying air within the dryer is regulated using adjustable air blast blades in the entrance section of the drying chamber. An appropriate configuration of these flow adjusting devices is proposed so that an adequately uniform drying air distribution pattern inside the drying chamber is achieved. Finally, a characteristic design case is presented to demonstrate the effectiveness of the proposed approach.     

Optimizing the catalyst distribution for countercurrent methane steam reforming in plate reactors

Microscale autothermal reactors remain one of the most promising technologies for efficient hydrogen generation. The typical reactor design alternates microchannels where reforming and catalytic combustion of methane occur, so that exothermic and endothermic reactions take place in close proximity. The influence of flow arrangement on the autothermal coupling of methane steam reforming and methane catalytic combustion in catalytic plate reactors is investigated. The reactor thermal behavior and performance for cocurrent and countercurrent are simulated and compared. A partial overlapping of the catalyst zones in adjacent exothermic and endothermic channels is shown to avoid both severe temperature excursions and reactor extinction. Using an innovative, optimization‐based approach for determining the catalyst zone overlap, a solution is provided to the problem of determining the maximum reactor conversion within specified temperature bounds, designed to preserve reactor integrity and operational safety. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

A centroid‐based sampling strategy for kriging global modeling and optimization

A new sampling strategy is presented for kriging‐based global modeling. The strategy is used within a kriging/response surface (RSM) algorithm for solving NLP containing black‐box models. Black‐box models describe systems lacking the closed‐form equations necessary for conventional gradient‐based optimization. System optima can be alternatively found by building iteratively updated kriging models, and then refining local solutions using RSM. The application of the new sampling strategy results in accurate global model generation at lower sampling expense relative to a strategy using randomized and heuristic‐based sampling for initial and subsequent model construction, respectively. The new strategy relies on construction of an initial kriging model built using sampling data obtained at the feasible region's convex polytope vertices and centroid. Updated models are constructed using additional sampling information obtained at Delaunay triangulation centroids. The new sampling algorithm is applied within the kriging‐RSM framework to several numerical examples and case studies to demonstrate proof of concept. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

陶瓷制品三维造型计算机辅助设计(三)

本文对真实感陶瓷制品三维造型计算机辅助设计进行了研究,对陶瓷制品采用Torrance—Sparrow法进行光照处理,得到了真实感逼真的三维造型设计。     

Optimization problem with normally distributed uncertain parameters

The issue of chemical process optimization when at the operation stage the design specification should be met with some probability and the control variables can be changed has been considered. A common approach for solving the broad class of optimization problems with normally distributed uncertain parameters were developed. This class includes the one‐stage and two‐stage optimization problems with chance constraints. This approach is based on approximate transformation of chance constraints into deterministic ones. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2471–2484, 2013     

A multiperiod nonlinear programming approach for operation of air separation plants with variable power pricing

Cryogenic air separation processes consume a large amount of electricity producing significant quantities of high purity gases. Rather than operating at a fixed steady state, it may be profitable to switch among different operating conditions because of variability of electrical prices and product demands. This article addresses the problem of determining the optimal daily multiperiod operating conditions for an air separation process under variable electricity pricing and uncertain product demands. The multiperiod nonlinear programming formulation includes a rigorous nonlinear model of the highly‐coupled process, and decision variables include the operating conditions within each period, as well as the transition times. Demand uncertainty is treated using the loss function included in the objective function and constraints on customer satisfaction levels. Solutions are obtained with high computational efficiency, allowing management to make informed decisions regarding operating strategies while considering the trade off between profitability and customer satisfaction levels. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Integrated crude selection and refinery optimization under uncertainty

Crude oil selection and procurement is the most important step in the refining process and impacts the profit margin of the refinery significantly. Due to uncertain quality of the crudes, conventional deterministic modeling and optimization methods are not suitable for refinery profitability enhancement. Therefore, a novel optimization scheme for crude oil procurement integrated with refinery operations in the face of uncertainties is presented. The decision process comprises two stages and is solved using a scenario‐based stochastic programming formulation. In Stage I, the optimal crude selections and purchase amounts are determined by maximizing the expected profit across all scenarios. In Stage II, the uncertainties are realized and optimal operations for the refinery are determined according to this realization. The resulting large‐scale mixed‐integer nonlinear programming formulation incorporates integer variables for crude selection and continuous variables for refinery operations, as well as bilinear terms for pooling processes. Nonconvex generalized Benders decomposition is used to solve this problem to obtain an global optimum efficiently. © 2015 American Institute of Chemical Engineers AIChE J, 62: 1038–1053, 2016     

Batch-centric model for scheduling straight multisource pipelines

This article develops a highly efficient batch-centric model for the detailed scheduling of straight multiple-source pipelines. The continuous-time model permits multiple batches to be injected/delivered over a slot, thus enabling finding better schedules for a given number of event points in the grid. It can consider either global or line batch numbering. In the former, one predefines empty batches to allow injection of new products at intermediate sources. Empty batches are avoided with line numbering at the expense of specifying a number of batches for every line in the system, instead of using a global value. Three benchmark problems from the literature are solved to evaluate the performance of the proposed formulation. Compared to our recent work, new best solutions are reported in two cases, while for the third, the computational time has been reduced by two orders of magnitude. The results also show that global batch numbering is better.     

气辅产品质量的多指标模糊综合评判决策及优选策略研究

针对影响气辅产品质量的因素多的难题,应用模糊综合评价原理并结合正交实验方法,提出了将模糊综合评判和效应工程方法相结合的优选策略,结合气辅技术的具体特点和专家意见来确定权重向量,设计了基于多指标评判的模糊优选算法模型。通过对气辅产品工艺方案的分析和实验方案的设计,结合具体实例验证了这种算法是可行的,可以较快、较准确地找出最优的气辅产品及相应的工艺方案。     

Estimation of mass-based composition in powder mixtures using Extended Iterative Optimization Technology (EIOT)

The Extended Iterative Optimization Technology (EIOT) method is proposed as an extension to Muteki's [I&ECR 2013;52(35):12258–12268] Iterative Optimization Technology to address deviations from Beer–Lambert's law in powders. The new method estimates the apparent spectrum for the pure species, rather than using the measured spectrum and augments Beer–Lambert's law with a bilinear term to capture the signature and strength of the nonchemical interferences. The proposed method has exhibited acceptable performance in spite of using a lean data set to estimate its parameters. The method provides robust and coherent estimates within the physical boundaries of the system and exhibits robustness to instrument transfer. The lean effort needed to build the EIOT method positions it as an attractive option in early stages of pharmaceutical drug product development. Its robustness to distinguish chemical from nonchemical signals implies a potential to lower the total cost of ownership for an EIOT-based solution in manufacturing. © 2018 American Institute of Chemical Engineers AIChE J, 65: 87–98, 2019     

Modeling multistream heat exchangers with and without phase changes for simultaneous optimization and heat integration

A new equation‐oriented process model for multistream heat exchangers (MHEX) is presented with a special emphasis on handling phase changes. The model internally uses the pinch concept to ensure the minimum driving force criteria. Streams capable of phase change are split into substreams corresponding to each of the phases. A novel disjunctive representation is proposed that identifies the phases traversed by a stream during heat exchange and assigns appropriate heat loads and temperatures for heat integration. The disjunctive model can be reformulated to avoid Boolean (or integer) variables using inner minimization and complementarity constraints. The model is suitable for optimization studies, particularly when the phases of the streams at the entry and exit of the MHEX are not known a priori. The capability of the model is illustrated using two case studies based on cryogenic applications. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Kinetic parameter estimation based on spectroscopic data with unknown absorbing species

In many studies, kinetic parameter estimation from spectroscopic data is performed with the absorbing species known beforehand, as this provides a straightforward link between the reaction models to the spectroscopic data. In practice, however, the absorbing species are generally unknown and they are only estimated based on professional experience and prior knowledge of the kinetic reaction. In this work, we propose an optimization strategy with both continuous and discrete decision variables in order to estimate kinetic parameters from spectroscopic data with unknown absorbing species. Also included in our approach is an estimability analysis for kinetic parameters based on the Gram‐Schmidt orthogonalization procedure, along with covariance estimation. Four case studies were considered, which demonstrate the effectiveness of our approach. The first and second have simulated data and illustrate our approach with known solutions. The third and fourth are based on actual experiments from spectroscopy data sets. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3595–3613, 2018     

Reduced Hessian based parameter selection and estimation with simultaneous collocation approach

Parameter estimation is a critical step in the building of process models. Given the nonlinear structure and limited measurements, it is often difficult to correctly estimate all the parameters involved in the model. Linear dependence and low correlation among the parameters are the main problems to be handled in parameter estimation. The common approach is to estimate a subset of the parameters by fixing the others at reasonable values. However, it is a challenge to determine which parameters can be properly estimated. In this work, the ratio between standard deviation and estimated parameter value is introduced for evaluating the estimability. A Gauss-Jordan elimination based approach is proposed for parameter estimability ranking. Combined with the proposed ranking approach and approximate ratio criterion, a reduced Hessian based approach is proposed for parameter selection and estimation under a simultaneous collocation framework. The proposed approach is at least as effective and more efficient than competing approaches based on multiple eigenvalue decompositions or orthogonalizations for larger problems. Three case studies with increasing complexity are presented to demonstrate the performance of the proposed approach.     

A quadratic approximation‐based algorithm for the solution of multiparametric mixed‐integer nonlinear programming problems

An algorithm for the solution of convex multiparametric mixed‐integer nonlinear programming problems arising in process engineering problems under uncertainty is introduced. The proposed algorithm iterates between a multiparametric nonlinear programming subproblem and a mixed‐integer nonlinear programming subproblem to provide a series of parametric upper and lower bounds. The primal subproblem is formulated by fixing the integer variables and solved through a series of multiparametric quadratic programming (mp‐QP) problems based on quadratic approximations of the objective function, while the deterministic master subproblem is formulated so as to provide feasible integer solutions for the next primal subproblem. To reduce the computational effort when infeasibilities are encountered at the vertices of the critical regions (CRs) generated by the primal subproblem, a simplicial approximation approach is used to obtain CRs that are feasible at each of their vertices. The algorithm terminates when there does not exist an integer solution that is better than the one previously used by the primal problem. Through a series of examples, the proposed algorithm is compared with a multiparametric mixed‐integer outer approximation (mp‐MIOA) algorithm to demonstrate its computational advantages. © 2012 American Institute of Chemical Engineers AIChE J, 59: 483–495, 2013