Digital control of a rotary dryer in the sugar industry

In order to utilize pulp, which is a by-product of the extraction of sugar from beet, one has to dry it. This is usually done with a rotary dryer. Generally the dry substance percentage of the dried pulp is controlled manually; this, however, is not sufficient. The aim was to improve the quality of control and thereby to reduce the fuel consumption. To reach this goal, first a linear, time-invariant, discrete-time mathematical model of the dynamic behaviour of the drying process was identified off-line. A correlation analysis method with least-squares parameter estimation was used. Based on the identified model, the parameters of the control algorithms of a digital multicascaded control system were designed with computer aid. The control behaviour was simulated and compared with the control behaviour achieved by an optimal state feedback control system. Although the latter one gave a better control performance, the cascaded control system was implemented on a process control computer—this was done for practical reasons. The results and experience gained during three campaigns show that the goal, a significant improvement of the control performance and the resulting saving in fuel oil, was achieved.     

Identification and predictive control of a multistage evaporator

A recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Input–output data from system identification experiments are used in training the network using the Levenberg–Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC–PI control scheme.     

基于单元模型的神经网络预测控制及其应用

针对电站锅炉这样一个非线性耦合、大滞后、多变量、多干扰的复杂对象,根据单元控制的思想,并运用神经网络预测控制的方法,应用于过热蒸汽温度控制中。并给出了将它应用于锅炉主蒸汽温度控制的仿真结果。     

FCS系统在蔗糖行业中的应用

针对广西蔗糖业的巨大前景,为了提高总体技术水平,缩小与发达国家和国际先进水平的差距,对该行业的一些关键工段进行自动化控制是目前能够实现也完全能达到国际水平的。通过FCS2000现场总线控制系统在广西蔗糖业的成功应用,反映了国产FCS2000现场总线控制系统的系统功能的实用性和稳定性,实现了对蔗糖生产中蔗渣燃烧锅炉及其相关的自动控制,提高了管理水平和自动化程度,降低了单位能耗,节约了资源。为广西蔗糖业的发展贡献了一份力量。     

Optimization control for the coordinated system of an ultra-supercritical unit based on stair-like predictive control algorithm

This study proposes an optimal control scheme for the coordinated control system (CCS) of an ultra-supercritical unit. This scheme utilizes a stair-like predictive control algorithm as the core to solve fundamentally the control problem of large delay and inertia in boiler combustion and integrates the feedforward and decoupling control concepts to preserve the traditional control experience. This study aims to provide technical support for the clean and efficient use of coal and the large-scale consumption of renewable energy sources in China. Non-real-time and real-time simulation results show that the scheme can achieve optimal control for each controlled variable, especially in the decoupling control mode; moreover, the fluctuation of each controlled variable is considerably reduced, and the adjustment of each control variable is stable, thereby improving the stability and anti-interference capability of the CCS. From the perspective of practical application of engineering, the research results have been directly productized and applied to practical engineering, the engineering application shows that the control performance is basically consistent with the simulation, the response rate of the unit is considerably increased, and the fluctuation of main steam pressure and mid-point temperature is considerably reduced. In this case, the proposed scheme can guarantee the safety, stability, economy, and flexibility of the unit operation.     

刨煤机组支架电液控制关键

介绍了刨煤机组支架电液控制系统结构,重点阐述了支架电液控制关键,包括推移精度控制、快速移架方法及最优梁端距排列控制,给出了相应的解决方案。实践表明,刨煤机组支架电液控制系统能够精准控制支架自动推移步距,移架时间约为8s,且工作面未出现掉顶或片帮现象。     

Nonlinear model predictive control of biodiesel production via transesterification of used vegetable oils

The economic performance of an industrial scale semi-batch reactor for biodiesel production via transesterification of used vegetable oils is investigated by simulation using nonlinear model predictive control (NMPC) technology. The objective is to produce biodiesel compliant to the biodiesel standards at the minimum costs. A first-principle model is formulated to describe the dynamics of the reactor mixture temperature and composition. The feed oil and mixture composition are characterized using a pseudo-component approach, and the thermodynamic properties are estimated from group contribution methods. The dynamic model is used by the NMPC framework to predict the optimal control profiles, where a multiple shooting based dynamic optimization problem is solved at every sampling time. Simulation results with the economic performance of an industrial scale semi-batch reactor are presented for control configurations manipulating the methanol feed flow rate and the heat duty.     

On a generalized predictive control algorithm

Generalized predictive control algorithms with reference models on imputs and outputs of the process have been proposed recently in the literature. Thise algorithms are extended by introducing suitable weighting factors in the performance index and it is shown that such algorithms provide a combined feedback feedforward control resulting in pole-zero cancellation of poles which do not correspond to the reference model. Hence, the system behaves asymptotically as the reference model provided the cancelled poles are stable. Therefore, a careful analysis of the stability of those poles in still needed.     

On modelling and control of a rotary sugar dryer

This paper deals with the problem of fitting a set of data collected on a rotary sugar dryer, by means of a first-principles mathematical model. Owing to the highly constrained structure of the model, it was discovered that the sugar dryer is characterised by two different working conditions: the “standard-mode” (characterised by a non-zero sugar moisture content), and the “overdried-mode” (namely a condition where the sugar moisture content is almost nil, and the evaporative phenomenon becomes negligible). On the basis of this two-stage behaviour, an accurate fit between the model and the measured data can be achieved, and an innovative control strategy can be drawn.     

Optimal control of nonlinear systems: a predictive control approach

A new nonlinear predictive control law for a class of multivariable nonlinear systems is presented in this paper. It is shown that the closed-loop dynamics under this nonlinear predictive controller explicitly depend on design parameters (prediction time and control order). The main features of this result are that an explicitly analytical form of the optimal predictive controller is given, on-line optimisation is not required, stability of the closed-loop system is guaranteed, the whole design procedure is transparent to designers and the resultant controller is easy to implement. By establishing the relationship between the design parameters and time-domain transient, it is shown that the design of an optimal generalised predictive controller to achieve desired time-domain specifications for nonlinear systems can be performed by looking up tables. The design procedure is illustrated by designing an autopilot for a missile.     

The development of a truck scale management system for the sugar industry in Trinidad and Tobago

Summary

The paper reviews a project on the development of a Truck Scale Management System (TSMS) for Caroni (1975) Limited, a State Enterprise which is seeking to improve efficiency and consequently reduce costs in its operation.

The paper also provides a background on factors which significantly affect process automation. The most important of these relate to the lack of awareness by top management of the varied use and benefits of microelectronics and information technology, shortage of relevant human resources and insufficient investment incentives.

Automation of the weighbridge was given high priority by Caroni's decision makers because of allegations of falsification of weights, the inability to get timely and adequate information for decision making and the labour intensiveness of the manual system. A project was undertaken to design, procure, install, commission and operate an unmanned TSMS at both factories using local personnel to execute the project. Caroni formed a multi‐disciplinary team from in‐house, a publicly funded research institution and a local supplier. The team designed the system (providing a number of options), assessed the technology, acquired a number of microprocessor‐based modules including load cell technology, bar code technology, magnetic card readers and modems, and implemented the system as two separate pilots. Software was developed by Caroni to integrate all these systems. The team was successful in demonstrating the fully automated system under crop conditions but it has not been implemented in completeness as the decision makers required additional trials.

Cane operators were trained in keyboard data entry and the users, including contractors, were taken through the procedures before the system was put on stream. Caroni has organized training programmes involving this new technology for its engineers and technicians, aimed mainly at integrating new technologies with existing skills. The system could be commercialized. One cane producing country has shown interest in acquiring the technology. It is possible to package the system and target it for applications in activities such as grain and cement weight controls.

Some of the spin‐offs from the experience gained from the project include the automation of Caroni's juice scales at both factories, proposed automation of the juice clarification, evaporation and sugar boiling, and automation of the laboratories.

The system has gained acceptance by all those who interfaced with it. Caroni has had no adverse reaction to the TSMS from the workers but for future automation projects would have to develop strategies of redeployment and retraining to avoid the backlash of resentment and opposition which could hinder progress.     

Surge tank control in a cane raw sugar factory

The performance of the juice circuit in a cane raw sugar factory is critical to the production of high quality sugar, and hence its saleability on the international market. In this paper, we present a control project that deals with this aspect of the raw sugar production process. The problem that is addressed is to control surge tanks within the juice circuit in order to steady the flow (minimisation of the rate of change) into a settling vessel known as the clarifier. This is a particularly important problem since the clarifier’s efficiency is reduced by rapid flow rate variations with the ultimate result being a reduction in raw sugar quality due to increased extraneous matter in the sugar. The new solution involves using gain-scheduled feedforward techniques to control the existing surge tanks so that the available capacity is used in an efficient way. The new control scheme was implemented on existing hardware and achieved the desired level of performance.     

Optimizing transportation and storage of final products in the sugar and ethanol industry: a case study

COPERSUCAR Ltda (the acronym for the Sugarcane and Ethanol Producers' Cooperative in São Paulo state) is a Brazilian cooperative of sugarcane producers and the largest sugar and ethanol manufacturer in Brazil, producing 4.4 million metric tons of sugar and 2.7 billion liters of ethanol. The cooperative is composed of 34 sugar mills with centralized sales and marketing. This organization establishes the amount of each product that will be manufactured in each mill to reduce total transportation and storage costs and, consequently, increase overall gain. Critical aspects of this problem are seasonal production and, therefore, the need to store final products to meet demand during the off‐season period. This study focuses on the application of a multi‐period linear programming model that provides optimal assignment of production, transportation, and storage of final products subject to manufacturing and flow capacity constraints. The expected annual benefits of implementing the proposed solution are 3.3 million dollars. In addition, a sensitivity analysis was carried out to investigate the possibility of increasing the capacity of the installed mills.     

Analysis and control of a nonlinear boiler-turbine unit

A distance measure is proposed via the gap metric in this paper, and the concept is applied to a boiler-turbine unit to analyze its dynamics. It is shown that the unit shows severe nonlinearity, but the nonlinearity can be avoided by careful choice of the operating range. A single linear controller can be designed to work in such an operating range. It is also shown that the controller constraint is another source of the nonlinearity, which can be compensated using anti-windup techniques. Simulation results are given to verify the conclusions.     

Nonlinear predictive control in the LHC accelerator

This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.     

Model predictive control of a rotary cement kiln

A first principles model of a cement kiln is used to control and optimize the burning of clinker in the cement production process. The model considers heat transfer between a gas and a feed state via convection and radiation. Furthermore, it contains effects such as chemical reactions, feed transport, energy losses and energy input. A model predictive controller is used to stabilize a temperature profile along the rotary kiln, guarantee good combustion conditions and maximize production. Moving horizon estimation was used for online estimation of selected model parameters and unmeasured states. Results from the pilot site are presented.     

Adaptive predictive control of a multistage fermentation process

This paper reports on the application of adaptive control techniques to the control of real-life biotechnological processes. The problem of regulation of the substrate concentration in the effluent of a multistage fermentation plant is considered in view of recent adaptive control concepts. The direct use of the process non-linear dynamics together with the on-line estimation of key process parameters gives rise to a robust (as BIBO stability analysis suggests) and efficient (as simulation and experimental results indicate) control scheme.     

Long-range predictive control of a rapid thermal processor

Long-range predictive control of a quartz chamber furnace used in microelectronic technology is discussed. It is based on a technique called rapid thermal processing (short thermal cycles, reaching high temperatures for a short time). The furnace temperature and the electric power supplied to the furnace are selected as controlled and control variables, respectively. The control algorithm is based on the minimization of a quadratic criterion involving the input and output traektng errors- it is derived in the receding-horizon sense. To represent the complex dynamics of the furnace, a single input/output controlled autoregressive integrating moving average model (CARIMA) was adopted. The model parameters are estimated on-line using a robust identification scheme which includes data normalization, covariance matrix factorization, adjustable forgetting factor, etc. Experimental results are included which demonstrate the capability of the adaptive control algorithm considered with rapid thermal processes.     

An implementation of generalized predictive control in a flotation plant

A single-input/single-output (SISO) GPC algorithm and an ARX model identification routine were programmed in the form of a portable “module” on a graphical computing environment. Although attractive from an end-user (operator) standpoint, the selected platforms presented some programming challenges. The whole GPC module (controller and identification) was implemented on an industrial control system and applied to many sulfide flotation circuits in the Brunswick Mining concentrator (New Brunswick, Canada). The performance of the GPC controller was good on all the flotation circuits to which it was applied. Significant reductions of grade variance are reported and the controller exhibits good load and servo response.