Optimal control and operation of drum granulation processes

Process optimisation and optimal control of batch and continuous drum granulation processes are studied in this paper. The main focus of the current research has been: (i) construction of optimisation and control relevant, population balance models through the incorporation of moisture content, drum rotation rate and bed depth into the coalescence kernels; (ii) investigation of optimal operational conditions using constrained optimisation techniques; (iii) development of optimal control algorithms based on discretized population balance equations; and (iv) comprehensive simulation studies on optimal control of both batch and continuous granulation processes. The objective of steady state optimisation is to minimise the recycle rate with minimum cost for continuous processes. It has been identified that the drum rotation-rate, bed depth (material charge), and moisture content of solids are practical decision (design) parameters for system optimisation. The objective for the optimal control of batch granulation processes is to maximize the mass of product-sized particles with minimum time and binder consumption. The objective for the optimal control of the continuous process is to drive the process from one steady state to another in a minimum time with minimum binder consumption, which is also known as the state-driving problem. It has been known for some time that the binder spray-rate is the most effective control (manipulative) variable. Although other possible manipulative variables, such as feed flow-rate and additional powder flow-rate have been investigated in the complete research project, only the single input problem with the binder spray rate as the manipulative variable is addressed in the paper to demonstrate the methodology. It can be shown from simulation results that the proposed models are suitable for control and optimisation studies, and the optimisation algorithms connected with either steady state or dynamic models are successful for the determination of optimal operational conditions and dynamic trajectories with good convergence properties.     

Predictive control of particle size distribution in particulate processes

In this work, we focus on the development and application of predictive-based strategies for control of particle size distribution (PSD) in continuous and batch particulate processes described by population balance models (PBMs). The control algorithms are designed on the basis of reduced-order models, utilize measurements of principle moments of the PSD, and are tailored to address different control objectives for the continuous and batch processes. For continuous particulate processes, we develop a hybrid predictive control strategy to stabilize a continuous crystallizer at an open-loop unstable steady-state. The hybrid predictive control strategy employs logic-based switching between model predictive control (MPC) and a fall-back bounded controller with a well-defined stability region. The strategy is shown to provide a safety net for the implementation of MPC algorithms with guaranteed stability closed-loop region. For batch particulate processes, the control objective is to achieve a final PSD with desired characteristics subject to both manipulated input and product quality constraints. An optimization-based predictive control strategy that incorporates these constraints explicitly in the controller design is formulated and applied to a seeded batch crystallizer. The strategy is shown to be able to reduce the total volume of the fines by 13.4% compared to a linear cooling strategy, and is shown to be robust with respect to modeling errors.     

Online measurement and control of solids moisture in fluidised bed dryers

Currently, two main methods are used to take online measurement of the solids moisture in fluidised bed dryers, namely microwave resonance and near infrared spectroscopy. In this paper, a new online approach to solids moisture measurement of batch fluidised bed dryers by electrical capacitance tomography (ECT) is presented for the first time. Based on online measurement of solids moisture, it is possible to implement feedback control and process optimisation of batch fluidised bed drying processes, aiming to increase the operation efficiency and to improve product quality. A twin-plane ECT sensor with eight electrodes in each plane is mounted in the bottom of a glass fluidisation chamber. From the adjacent electrode pairs, the water content of the solids is estimated based on the correlation between the moisture content and the permittivity value. To reduce measurement error, the effect of temperature on moisture measurement is compensated. The fluidisation velocity is estimated by a semi-empirical function based on the measured water content. The acquired information is sent to a controller to adjust the air flow rate of the fluidised bed dryer. To validate the moisture measurement by ECT, a mathematical model has been developed, based on the measured temperature and relative humidity of the outlet air. The Landweber iteration method is applied to reconstruct images. The averaged solids concentration along the radial direction at different fluidisation conditions is given and compared with results by the linear back-projection (LBP) method. Results from batch drying processes with online measurement and feedback control are given and compared with no feedback control. To compare the operation efficiency, the thermal efficiency is considered and the results show the possibility of online control and optimisation of the fluidised bed drying processes, based on online measurement of solids moisture by ECT. Some challenges and future work are discussed.     

Design and optimisation of batch and semi-batch reactors

This paper addresses a systematic methodology for batch and semi-batch reactor design and optimisation for both ideal and non-ideal mixing. It can be applied to non-isothermal and multiphase systems. The method starts from a general representation in the form of a temporal superstructure based on the similarity of between plug flow reactors and ideal batch reactors. The temporal superstructure of a batch reactor exists in both the space and time dimensions. For non-ideal mixing, this paper addresses a mixing compartment network model to represent mixing inside reactors. The mixing compartment network is then included into the temporal superstructure to model non-ideally mixed batch reactors and the mixing pattern optimised with the other variables. Besides the operation variables for batch reactors, this method can also suggest the optimum mixing pattern and promising reactor configurations for mechanical design. A profile-based approach is proposed to make a search of the profiles for temperature, pressure and feed addition. This approach starts from a set of initial profiles of temperature, pressure and feed addition. Then the performance of the batch reactor is evaluated against the objective function under different profiles. An optimal set of profiles is then found by this profile searching process. A stochastic optimisation technique based on simulated annealing is employed to obtain optimal solutions. This method is also extended to multiphase reaction systems based on the concept of shadow reactor compartments. A number of case studies are presented to illustrate the use of the proposed methodology.     

Asynchronous optimisation with the use of a cascade search algorithm

This paper introduces the development of an asynchronous approach coupled with a cascade optimisation algorithm. The approach incorporates concepts of asynchronous Markov processes and introduces a search process that is benefiting from distributed computing infrastructures. The algorithm uses concepts of partitions and pools to store intermediate solutions and corresponding objectives. Population inflections are performed periodically to ensure that Markov processes, still independent and asynchronous, make arbitrary use of intermediate solutions. Tested against complex optimisation problems and in comparison with commonly used Tabu Search, the asynchronous cascade algorithm demonstrates a significant potential in distributed operations with favourable comparisons drawn against synchronous and quasi-asynchronous versions of conventional algorithms.     

Exergetic criteria in process optimisation and process synthesis: Opportunities and limitations

The exergy concept is a well established way to express the quality of any kind of process stream, be it energy or matter. Thus detailed exergy analysis of chemical processes can in general be performed and several such applications have been reported in the literature. However, systematic methods for exergetic process optimisation only exist for the comparably less complex area of power systems. For chemical processes a solid optimisation approach based on exergy analysis has yet to be derived. This paper presents a thorough investigation of opportunities and limitations of the exergy concept in both process optimisation and process synthesis. The two tasks are treated separately since their specific requirements and solution strategies are different. As a result of the presented evaluation, concepts for the efficient use of exergetic criteria are proposed.     

Multi-period design of heat exchanger networks

Heat exchanger networks are an integral part of chemical processes as they recover available heat and reduce utility consumption, thereby improving the overall economics of an industrial plant. This paper focuses on heat exchanger network design for multi-period operation wherein the operating conditions of a process may vary with time. A typical example is the hydrotreating process in petroleum refineries where the operators increase reactor temperature to compensate for catalyst deactivation. Superstructure based multi-period models for heat exchanger network design have been proposed previously employing deterministic optimisation algorithms, e.g. (0005 and 0180). Stochastic optimisation algorithms have also been applied for the design of flexible heat exchanger networks recently (0110 and 0115). The present work develops an optimisation approach using simulated annealing for design of heat exchanger networks for multi-period operation. A comparison of the new optimisation approach with previous deterministic optimisation based design approaches is presented to illustrate the utilisation of simulated annealing in design of optimal heat exchanger network configurations for multi-period operation.     

Profitability as a criterion of batch process control design

Batch processes have in some cases many advantages in comparison with continuous processes even though continuous processes are becoming common.

The main disadvantages of batch processes are the discontinuous usage of raw materials and energy as well as the discontinuous production thus causing difficulties in power plant and other continuous processes connected with the batch process in question. If there are several parallel process units, difficulties can arise with parallel process unit sequencing and product quality equalization. However, with the aid of computer control these and other disadvantages are eliminated or minimized so that total automation of batch processes is possible.

In this paper the basic principles of batch process control design are considered, with particular emphasis on the economic justification criterion. As an example, a computer control design of sulphate batch digesters is considered. This approach is based on more than 20 implementations of batch process automation.     

Multi-objective optimisation of batch separation processes

This paper considers for the first time the simultaneous multi-objective optimisation of design and operation of batch distillation as well as of batch hybrid distillation/pervaporation processes. The overall problem is formulated as a multi-objective mixed integer dynamic optimisation (MO-MIDO) problem. The optimisation strategy comprises of different ranking procedures that allow the determination of the Pareto optimal set. A case study for the separation of a homogeneous tangent-pinch (acetone–water) mixture is presented for a dual-criteria optimisation case of minimising capital investment while at the same time minimising the energy consumption rate during the batch. It is found that the proposed distance ranking procedure yields the best Pareto optimal set when compared to other non-dominated sorting procedures. Furthermore, the distance ranking procedure was found to be further improved when used with an elitism operator.     

Optimal control of batch processes using particle swam optimisation with stacked neural network models

An optimal control strategy for batch processes using particle swam optimisation (PSO) and stacked neural networks is presented in this paper. Stacked neural network models are developed form historical process operation data. Stacked neural networks are used to improve model generalisation capability, as well as provide model prediction confidence bounds. In order to improve the reliability of the calculated optimal control policy, an additional term is introduced in the optimisation objective function to penalize wide model prediction confidence bounds. The optimisation problem is solved using PSO, which can cope with multiple local minima and could generally find the global minimum. Application to a simulated fed-batch process demonstrates that the proposed technique is very effective.     

Étude de l'aspect cornbinatoire de la gestion des conflits dans un atelier discontinu

The combinatorial angle of management of batch processes or job‐shop for fine chemicals is presented. Indeed, the order of priority for product recipes, which allows one to determine the first batch to be loaded, has a strong combinatorial aspect. When dealing with stochastic algorithms, this aspect requires a precise and non‐redundant numbering of the orders of priority. A fairly complex mathematical study based on a family of pseudo‐orthogonal polynomials provides a non‐recursive formula easy to implement.     

Scale-up of binder agglomeration processes

This review article describes scale-up of batch and continuous granulation processes where liquid binder is added to fine powder in order to form a granular product. The technical goal of scale-up is to maintain similarity of critical product attributes as the production scale and/or throughput of a manufacturing process is increased. This paper provides a framework for scaling-up that considers critical process transformations in relation to the desired product attributes. A similar approach can be taken in developing process control strategies. In any agglomeration process, transformations can be used to describe how raw materials (typically fine powders and liquid binders) are converted into a granular product. Often the critical product attributes are characterized on the scale of individual granules (e.g., size, shape, porosity, mechanical strength, etc.). On the other hand, industrial scale-up requires predictive relations for the sizing, design and operation of process equipment. Considering scale-up on the basis of transformations is one way to link the macro-scale equipment decisions with micro-scale product attributes. This approach can be applied to the scale-up of batch and/or continuous granulation processes as well as transitioning from small batch prototypes to continuous production circuits.     

应用Aspen Batch对年产25吨鲁拉西酮原料药工艺设计优化

采取专利CN 102863437 A的工艺,设计间歇工艺过程,将Aspen Batch Process Developer软件应用于盐酸鲁拉西酮原料药车间设计的全流程模拟和优化。间歇操作具有显著的优势：生产灵活,同一设备可生产不同产品,可根据市场需要调节生产能力及变更产品。适合于小批量,高收益的精细化学品。过去的四十年里,使用计算机对化工连续化生产进行模拟和设计已经十分普及。制药工业与传统化工最大的区别是生产过程多采用间歇法操作。目前世界上应用于化工间歇生产的计算机软件有BATCHES、gPROMS和Aspen Batch Process Developer。本文所用版本为Aspen Tech V8.6,以年产25t盐酸鲁拉西酮原料药车间为例,对车间进行全流程模拟及优化。整个设计贯彻质量源于设计理念,运用元葱模型,将盐酸鲁拉西酮的生产工艺分为磺化、氨解、氢化、缩合、成盐、精烘包等6个模块。     

Subspace identification for data‐driven modeling and quality control of batch processes

In this work, we present a novel, data‐driven, quality modeling, and control approach for batch processes. Specifically, we adapt subspace identification methods for use with batch data to identify a state‐space model from available process measurements and input moves. We demonstrate that the resulting linear time‐invariant (LTI), dynamic, state‐space model is able to describe the transient behavior of finite duration batch processes. Next, we relate the terminal quality to the terminal value of the identified states. Finally, we apply the resulting model in a shrinking‐horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state‐of‐the‐art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate polymerization reactor. Results for both disturbance rejection and set‐point changes (i.e., new quality grades) are demonstrated. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1581–1601, 2016     

一类间歇生产过程的迭代学习控制算法及其收敛性分析

针对基于迭代学习控制的间歇过程产品质量优化控制算法难以进行收敛性分析的难题,并且考虑到实际生产中存在外部干扰和不确定因素的影响,本文对间歇过程模型参数动态更新问题进行了分析,建立了间歇生产过程产品质量的神经模糊(NF)预测模型,提出了一种新颖的批次轴参数自适应调节算法。在此基础上,构造了一种基于数据驱动的间歇生产过程产品质量迭代学习控制算法,并对优化问题的收敛性给出了严格的数学证明。最后,将本文提出的算法用于一类典型的间歇过程终点质量控制研究,仿真结果验证了本文算法的有效性和实用价值,为间歇过程的优化控制提供了一条新途径。     

Recursive data-based prediction and control of product quality for a PMMA batch process

In many batch processes, frequent process/feedstock disturbances and unavailability of direct on-line quality measurements make it very difficult to achieve tight control of product quality. Motivated by this, we present a simple data-based method in which measurements of other process variables are related to end product quality using a historical data base. The developed correlation model is used to make on-line predictions of end quality, which can serve as a basis for adjusting the batch condition/time so that desired product quality may be achieved. This strategy is applied to a methyl methacrylate (MMA) polymerization process. Important end quality variables, the weight average molecular weight and the polydispersity, are predicted recursively based on the measurements of reactor cooling rate. Subsequently, a shrinking-horizon model predictive control approach is used to manipulate the reaction temperature. The results in this study show promise for the proposed inferential control method.     

STATE ESTIMATION IN THE BATCH DRYING OF FOODS

Measurement of material moisture content is necessary for the control of product quality in batch drying. However, this variable cannot be measured on-line, and state estimation techniques are proposed. A non-linear dynamic model is developed for batch drying of foods. Process disturbances and measurement errors are modeled as stochastic processes and a hybrid extended Kalman filter is employed for state estimation. This filter is based on the local linearization of the process model around the suboptimal filter estimates. The moisture estimation approach was applied to experimental points obtained in a laboratory dryer with quite satisfactory results.     

Robust identification and control of batch processes

An approach is proposed for the robust identification and control of batch and semibatch processes. The batch experiments used for model identification are designed by minimizing the magnitude of the parameter uncertainties, and the effect of these uncertainties on the product quality achievable by optimal control is used as a stopping criterion for the identification procedure. The optimal control approach incorporates a quantification of the impact of both parameter and control implementation uncertainties on the performance of the optimal control policy. The approach is applied to the nucleation and growth of crystals with multiple characteristic dimensions, where the nominal parameters used in the simulation study are quantified from experimental data.     

Rather the Rule than the Exception: Non-Convex Pareto Sets and their Navigation in Distillation Processes

Novel algorithms for adaptive approximation and interactive navigation of Pareto sets are applied to various homogeneous distillation processes with recycle streams within an industrial flowsheet simulator. The adaptive approximation scheme shows that the Pareto set consists of both convex and non-convex regions when the product purities are maximised while minimising the total heat duties. It is illustrated how such Pareto sets can be navigated interactively using a ray tracing technique. Our results suggest that the occurrence of non-convex regions in the Pareto sets is due to recycle streams in the flowsheets and is therefore the rule rather than the exception in multi-objective optimisation of flowsheet simulations in chemical engineering.