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.     

Optimal grade transition and selection of closed-loop controllers in a gas-phase olefin polymerization fluidized bed reactor

To satisfy the diverse product quality specifications required by the broad range of polyolefin applications, polymerization plants are forced to operate under frequent grade transition policies. Commonly, the optimal solution to this problem is based on the minimization of a suitable objective function defined in terms of the changeover time, product quality specifications, process safety constraints and the amount of off-spec polymer, using dynamic optimization methods. However, considering the great impact that a given control structure configuration can have on the process operability and product quality optimization, the time optimal grade transition problem needs to be solved in parallel with the optimal selection of the closed-loop control pairings between the controlled and manipulated variables. In the present study, a mixed integer dynamic optimization approach is applied to a catalytic gas-phase ethylene-1-butene copolymerization fluidized bed reactor (FBR) to calculate both the “best” closed-loop control configuration and the time optimal grade transition policies. The gPROMS/gOPT computational tools for modelling and dynamic optimization, and the GAMS/CPLEX MILP solver are employed for the solution of the combined optimization problem. Simulation results are presented showing the significant quality and economic benefits that can be achieved through the application of the proposed integrated approach to the optimal grade transition problem for a gas-phase polyolefin FBR.     

DYNAMIC OPTIMAL CONTROL OF BATCH RICE DRYING PROCESS

The drying of paddy rice may result in quality degradation, expressed as a head kernel yield, leading to significant commercial depreciation of the product. A mathematical model of the drying and of the quality degradation process was combined with a dynamic optimization algorithm to determine the drying conditions (air temperature and relative humidity as functions of time) that ensured the highest possible final product quality for a specified drying time and a specified final moisture content. The robustness of the optimal drying strategy with respect to the initial state of the product, to the model parameters and to the initialization of the optimization algorithm was verified. The compromise between the highest achievable final quality and the allowed total drying time was studied. The combination of simulation and optimization yielded a new insight in the rice drying process and in the quality preservation strategies.     

Machine learning in drying

Abstract

Although very important for analysis of drying processes, physics-based models are limited in terms of their prediction ability and in most cases are unsuitable for real-time process control and optimization of industrial drying. In this paper, we provide an overview of the machine learning (ML) techniques and the state-of-the-art ML applications in drying of food and biomaterials. The applications include but not limited to data-driven models, nonlinear control and multi-objective optimization. The advantages of integration of ML with machine vision for real-time observation of product quality and fine-tuning control strategies are briefly discussed. Future research should focus on the integration of ML software tools with sensors to measure process and product variables. In addition, the drying research community should contribute towards building of open-source datasets, which is extremely important to leverage the power of ML algorithms. Integration of sensors, process analysis and software engineering will enable the development of “intelligent” drying systems.     

Application of Artificial Neural Networks (ANNs) in Drying Technology: A Comprehensive Review

Inspired by the functional behavior of the biological nervous system of the human brain, the artificial neural network (ANN) has found many applications as a superior tool to model complex, dynamic, highly nonlinear, and ill-defined scientific and engineering problems. For this reason, ANNs are employed extensively in drying applications because of their favorable characteristics, such as efficiency, generalization, and simplicity. This article presents a comprehensive review of numerous significant applications of the ANN technique to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in drying technology. We summarize the use of the ANN approach in modeling various dehydration methods; e.g., batch convective thin-layer drying, fluidized bed drying, osmotic dehydration, osmotic-convective drying, infrared, microwave, infrared- and microwave-assisted drying processes, spray drying, freeze drying, rotary drying, renewable drying, deep bed drying, spout bed drying, industrial drying, and several miscellaneous applications. Generally, ANNs have been used in drying technology for modeling, predicting, and optimization of heat and mass transfer, thermodynamic performance parameters, and quality indicators as well as physiochemical properties of dried products. Moreover, a limited number of researchers have focused on control of drying systems to achieve desired product quality by online manipulating of the drying conditions using previously trained ANNs. Opportunities and limitations of the ANN technique for drying process simulation, optimization, and control are outlined to guide future R&D in this area.     

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.     

Real‐time control of a semi‐industrial fed‐batch evaporative crystallizer using different direct optimization strategies

This article presents a model‐based control approach for optimal operation of a seeded fed‐batch evaporative crystallizer. Various direct optimization strategies, namely, single shooting, multiple shooting, and simultaneous strategies, are used to examine real‐time implementation of the control approach on a semi‐industrial crystallizer. The dynamic optimizer utilizes a nonlinear moment model for on‐line computation of the optimal operating policy. An extended Luenberger‐type observer is designed to enable closed‐loop implementation of the dynamic optimizer. In addition, the observer estimates the unmeasured process variable, namely, the solute concentration, which is essential for the intended control application. The model‐based control approach aims to maximize the batch productivity, as satisfying the product quality requirements. Optimal control of crystal growth rate is the key to fulfill this objective. This is due to the close relation of the crystal growth rate to product attributes and batch productivity. The experimental results suggest that real‐time application of the control approach leads to a substantial increase, i.e., up to 30%, in the batch productivity. The reproducibility of batch runs with respect to the product crystal size distribution is achieved by thorough seeding. The simulation and experimental results indicate that the direct optimization strategies perform similarly in terms of optimal process operation. However, the single shooting strategy is computationally more expensive. © 2010 American Institute of Chemical Engineers AIChE J, 57: 1557–1569, 2011     

Real time optimization with multivariable control is required to maximize profits

Optimization of an operating process unit performed in real time with an on-line process control computer has a number of significant advantages over an optimization executed by an off-line computer. These advantages are derived from the process control computer's abilities to know precisely the current state of the operation, to control the process at the set of interacting constraints representing the predicted future state of the operation, and to provide direct feedback on how valid the predictions actually are. Multivariable interactive computer control techniques which recognize constraints are required to hold the process at its most profitable operating conditions.     

利用状态空间模型和不敏卡尔曼滤波的分批补料发酵过程在线估计(英文)

On-line estimation of unmeasurable biological variables is important in fermentation processes, directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product. In this study, a novel strategy for state estimation of fed-batch fermentation process is proposed. By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model, a state space model is developed. An improved algorithm, swarm energy conservation particle swarm optimization (SECPSO), is presented for the parameter identification in the mechanistic model, and the support vector machines (SVM) method is adopted to establish the nonlinear measurement model. The unscented Kalman filter (UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process. The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.     

Manufacturing Better Quality Food Powders from Spray Drying and Subsequent Treatments

In this article three major aspects of spray-dried food powders are discussed. We first address several practical problems involved during spray drying that may greatly influence product quality. The second issue identifies how an accurate drying kinetics model can form a useful tool to predict changes in the physical and biological quality aspects and the microstructure of the particle during processing. Dryer-wide simulations using the accurate drying kinetics model can significantly reduce the number of experimental trials for optimizing the process. To date, such success has been restricted to production runs for pilot-scale or small-scale industrial operations. The final issue addresses some of the challenges encountered when evaluating the functionality of the spray-dried powders during their reconstitution. The superior functionality of the spray-dried food product needs to be established more scientifically, which can help commercial operations to achieve high-quality reconstitution.     

Optimal quality control of baker's yeast drying in large scale batch fluidized bed

Optimal quality control of drying process of baker's yeast in large scale batch fluidized bed dryer is presented using neural network based models and modified genetic algorithm (GA). The objective of this study is to determine optimal conditions to maximize product quality while minimizing energy consumption. For this purpose, the drying process and quality models based on neural network with delay units are combined for predicting the dry matter, product temperature, change in dry matter and the quality loss while minimizing energy consumption and this model is then used for optimal quality control. A stochastic method based optimization structure is designed in order to solve the optimization problem whose the objective function is discontinuous, non-differentiable, complex and highly non-linear. The results obtained by optimal quality control based on modified GA showed that the performance of the existing industrial scale drying process was improved. The constructed optimal quality control structure is very convenient for the production process applications and may be applied without too much modification.     

Evaluation and improvement of dynamic optimality in electrochemical reactors

A systematic approach for the dynamic optimization problem statement to improve the dynamic optimality in electrochemical reactors is presented in this paper. The formulation takes an account of the diffusion phenomenon in the electrode/electrolyte interface. To demonstrate the present methodology, the optimal time-varying electrode potential for a coupled chemical-electrochemical reaction scheme, that maximizes the production of the desired product in a batch electrochemical reactor with/without recirculation are determined. The dynamic optimization problem statement, based upon this approach, is a nonlinear differential algebraic system, and its solution provides information about the optimal policy. Optimal control policy at different conditions is evaluated using the best-known Pontryagin's maximum principle. The two-point boundary value problem resulting from the application of the maximum principle is then solved using the control vector iteration technique. These optimal time-varying profiles of electrode potential are then compared to the best uniform operation through the relative improvements of the performance index. The application of the proposed approach to two electrochemical systems, described by ordinary differential equations, shows that the existing electrochemical process control strategy could be improved considerably when the proposed method is incorporated.     

PRODUCT QUALITY MULTI-OBJECTIVE DRYER DESIGN

ABSTRACT

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.     

Soft sensors model optimization and application for the refinery real-time prediction of toluene content

Industrial facilities nowadays show an increasing need for continuous measurements, monitoring and controlling many process variables. The on-line process analyzers, being the key indicators of process and product quality, are often unavailable or malfunction. This paper describes development of soft sensor models based on the real plant data that could replace an on-line analyzer when it is unavailable, or to monitor and diagnose an analyzer’s performance. Soft sensors for continuous toluene content estimation based on the real aromatic plant data are developed. The autoregressive model with exogenous inputs, output error, the nonlinear autoregressive model consisted of exogenous inputs and Hammerstein–Wiener models were developed. In case of complex real-plant processes a large number of model regressors and coefficients need to be optimized. To overcome an exhaustive trial-and-error procedure of optimal model regressor order determination, differential evolution optimization method is applied. In general, the proposed approach could be, of interest for the development of dynamic polynomial identification models. The performance of the models are validated on the real-plant data.     

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.     

Process Design Method for Manufacturing of Natural Compounds and Related Molecules

Abstract

Natural active compounds from plants have an increasing economic meaning in all areas of life sciences like e.g., food additives, cosmetics, pharmaceuticals and crop protection. The main task in manufacturing larger amounts of these plant based products besides raw material supply, product stabilization, and quality assurance, is an efficient process development method and economic production technology. State of the art approach is used in standard extraction methods. Initially their quantities are determined in isolation of laboratory scale testing and then transferred into large‐scale production by keeping all operation parameters constant in order to keep product quality constant. Nowadays the problem is that in most cases besides any missing process optimization with regard to necessary economical objectives, equipment is used with no modern process control, automation or process integration design applied. This study is a first proposal for a systematical process development and design methodology.

It should address the need for efficient process evaluation of many compounds at an early stage and process optimization for manufacturing. Due to sustainability and economy the maximal yield contained of any target compounds in plants should be extracted, but this increases as well the side component amount and type extracted and thereby decreases product purity. Therefore, any approach to optimization, should be integrated with a process development and evaluation of additional purification steps. This article describes the experimental setup as well as modeling approaches, combined with experimental model parameter determination, to generate the basis for any total process optimization by simulation. As an example the extraction of target compounds from wood is chosen.     

Enhancing dynamic data reconciliation performance through time delays identification

In order to improve the performance of data reconciliation methods, an efficient Genetic algorithm (GA) for determining time delays has been developed. Delays are identified by searching the maximum correlation among the process variables. The delay vector (DV) is integrated within a dynamic data reconciliation (DDR) procedure based on Kalman filter through the measurements error model. The proposed approach can be satisfactorily applied not only off-line but also on-line. It was firstly validated in a dynamic process with recycles and feedback control loops. Then, the methodology was successfully applied to a highly non-linear and complex challenging control case study, the Tenessee Eastman benchmark process, demonstrating its robustness in complex industrial problems. This case study required to implement an extended Kalman filter to deal with the existing non-linearities.