Economic hybrid non-linear model predictive control of a dual circuit induced draft cooling water system

Petrochemical plants require the addition and removal of energy to and from the process and the movement of material to, from, and within the process piping and vessels. These fundamental mass and energy transfer requirements are typically achieved through the use of process utilities, which include electricity, steam, fuel gas, cooling water and compressed air. Utilities are responsible for a significant portion of the operating cost of a plant. Therefore, reduction in the consumption of utilities is a common process optimisation area. The situation is different when it comes to the generation and transportation of these utilities, which are often overlooked with regard to optimisation. In this paper, the potential benefits of utility optimisation are illustrated with particular focus on the generation and transportation areas. The main objectives are reductions in electrical energy consumption and cost and are illustrated for a dual circuit cooling water system. This system is non-linear and also hybrid in the sense that it contains both continuous and discrete input variables, which significantly complicates the design and implementation of control and optimisation solutions. This paper illustrates how the cost and energy consumption of a hybrid system can be reduced through the implementation of hybrid non-linear model predictive control (HNMPC) and economic HNMPC (EHNMPC). The results are compared to that of a base case and an Advanced Regulatory Control (ARC) case, showing that significant additional benefit may be achieved through the implementation of these advanced control and optimisation techniques. The paper further illustrates that additional capital is not necessarily required for the implementation of these techniques.     

Experimental application of nonlinear model predictive control: temperature control of an industrial semi-batch pilot-plant reactor

This paper describes the application of nonlinear model predictive control (NMPC) to the temperature control of a semi-batch chemical reactor equipped with a multi-fluid heating/cooling system. The strategy of the nonlinear control system is based on a constrained optimisation problem, which is solved repeatedly on-line by a step-wise integration of a nonlinear dynamic model and optimisation strategy. A supervisory control routine has been developed, based on the same nonlinear dynamic model, to handle automatically the fluid changeovers. Both NMPC and supervisory control have been implemented on a PC and applied to a 16 l batch reactor pilot plant. Experiments illustrate the feasibility of such a procedure involving predictive control and supervisory control.     

Optimisation of compressed air and electricity consumption in a complex robotic cell

Energy conservation of a complex robotic cell can be achieved in many different ways. Parameters related to different utilities that are used in complex robotic cell could be easily varied. Various utilities and various parameters should be observed as a part of an integral approach. Naturally, it is required to have in mind the productivity and efficiency of the entire robotic cell. This study presents the optimisation of a complex robotic cell with installed electric and pneumatic devices. The presented methodology involves three key steps: (1) an identification of possible approaches in complex robotic cell optimisation; (2) an identification of parameters of influence on electricity and compressed air consumption; and (3) experimental evaluation of the proposed approaches with a recommendation for the optimal one, meeting the requirements of maximal productivity and energy efficiency.     

多虚拟电厂接入的主动配电系统优化经济调度

在市场环境下,为促进含随机性可再生能源的消纳同时协调各经济主体矛盾并挖掘各主体价值和经济效益,本文提出一种基于分析目标级联理论的主动配网调度优化模型,以配网与虚拟电厂存在电能交互为建模背景。考虑虚拟电厂组成的多样性,设置风储型虚拟电厂以及含冷热电系统型虚拟电厂,各虚拟电厂可以分别与配网进行电能交互,各经济主体根据自身组成优化内部变量以及交互功率,通过分析目标级联法与其他主体协调交互功率实现分布式求解。最后通过IEEE33节点配电系统的算例仿真表明,调度结果能较好的反应主体经济性,得出风储型虚拟电厂有利于提高风电的消纳量,冷热电联供型虚拟电厂可以实现综合能源经济管理,分析目标级联法用于本文所提的模型背景是有效的。     

Lost in optimisation of water distribution systems? A literature review of system operation

Optimisation of the operation of water distribution systems has been an active research field for almost half a century. It has focused mainly on optimal pump operation to minimise pumping costs and optimal water quality management to ensure that standards at customer nodes are met. This paper provides a systematic review by bringing together over two hundred publications from the past three decades, which are relevant to operational optimisation of water distribution systems, particularly optimal pump operation, valve control and system operation for water quality purposes of both urban drinking and regional multiquality water distribution systems. Uniquely, it also contains substantial and thorough information for over one hundred publications in a tabular form, which lists optimisation models inclusive of objectives, constraints, decision variables, solution methodologies used and other details. Research challenges in terms of simulation models, optimisation model formulation, selection of optimisation method and postprocessing needs have also been identified.     

An integrated approach to active model adaptation and on-line dynamic optimisation of batch processes

In the application of on-line, dynamic process optimisation, adaptive estimation of the system states and parameters is usually needed to minimise the unavoidable model-process mismatch. This work presents an integrated approach to optimal model adaptation and dynamic optimisation, with specific focus on batch processes. An active approach is proposed whereby the input variables are designed so as to maximise the information content of the data for optimal model adaptation. Then, this active adaptation method is combined with the objective of process performance to form a multi-objective optimisation problem. This integrative approach is in contrast to the traditional adaptation method, where only the process performance is considered and adaptation is passively carried out by using the data as is. Two strategies for solving the multi-objective problem are investigated: weighted average and constrained optimisation, and the latter is recommended for the ease in determining the balance between these two objectives. The proposed methodology is demonstrated on a simulated semi-batch fermentation process.     

芳烃抽提装置换热网络节能改造

采用夹点技术对某芳烃厂芳烃抽提装置换热网络进行分析,根据夹点技术设计原则找出了其用能不合理的环节和原因,在完全依照夹点技术设计原则情况下,提出最大热回收换热网络改造方案,该方案可使整个芳烃抽提系统节约加热和冷却公用工程分别达到17．11％和19．08％,但改造投资费用较高。在参考夹点技术设计准则的同时考虑到现行的换热网络结构,提出了最小换热网络改动方案,该换热网络方案可节约加热和冷却公用工程分别为12．32％和10．26％,改造投资费用较低。两种方案的投资回收期都比较短。     

On an algorithm for combined system optimisation and parameter estimation

The paper is concerned with the determination of optimum steady-state operation of industrial plant where the optimisation is performed using a mathematical model with parameters whose values are estimated by comparing model and real plant measurements. The two associated problems of system optimisation and model parameter estimation are discussed and an algorithm is examined whose purpose is to accomplish the correct steady-state optimum operating condition on the real plant in spite of inaccuracies in the structure of the mathematical model. The aim of the paper is to investigate the performance of the algorithm which is accomplished through a theoretical analysis of its application to a linear process, where the optimisation is performed using a quadratic performance index and a mathematical model of incorrect structure. Particular emphasis is given to the stability and convergence properties of the algorithm and to the effect of real process measurement errors. Simulation results are also presented illustrating the effectiveness of the technique when applied to nonlinear optimisation problems including a study concerned with determining optimum controller set points to maximise the net rate of return from a chemical reactor plant.     

基于时序分析的阀冷进阀温度预测方法

电网在运行过程中,换流阀等关键设备会不断产生热量,当设备的热量不断聚集温度不断上升,会影响设备的稳定性和安全性,保证换流阀等关键设备稳定运行就显得至关重要.阀冷系统作为冷却系统的关键设备,以热导性较高的水为介质,将设备的热能带出,达到降低设备温度的目的.可以通过监控冷却水的温度、压力技术指标来确保换流阀安全、稳定运行.选取阀冷系统中的进阀温度为主要预测指标,对系统的历史数据进行充分的挖掘和分析,达到对电网运行状态预估的目的.将传统时序模型与机器学习结合提出ARIMA-SVM的混合模型,并与传统的ARIMA模型、SVM模型和GRU神经网络模型对中国南方电网的真实阀冷数据进行时序分析预测并进行对比实验.实验结果表明,ARIMA模型、SVM模型、GRU神经网络模型和ARIMA-SVM混合模型都可以较好地预测进阀温度的变化趋势,但ARIMA-SVM混合模型在均方根误差、均方误差和平均绝对误差3个评价指标上表现均更优于其他3个模型,能够进一步提升进阀温度预测的精度.     

Joint optimisation of application QoS and energy conservation in grid environment

Mobile and wireless devices with limited energy are now of interest for the grid community. Reducing the related energy consumption is very important for wireless devices. Compared to conventional grid systems, energy aware grids need to support application quality of service (QoS) with limited energy. There is an inherent conflict in the design goals for high grid application QoS and low energy consumption. To address this challenging problem, this article considers joint optimisation of application QoS and energy conservation in grid environment. Joint optimisation of application QoS and energy conservation is targeted to maximise the system utility without exceeding the deadline and the total energy available, which can be provided by an exhaustible source such as a battery. This article formulates joint optimisation of application QoS and energy conservation as constrained maximisation problem; the constraints include energy budget and application QoS requirements. This article also presents a QoS and energy aware scheduling algorithm (QESA) which balances application QoS and energy conservation to improve system performance. In the simulation, the performance evaluation of QESA is conducted.     

An autonomous decision support system for manganese forecasting in subtropical water reservoirs

Manganese monitoring and removal is essential for water utilities in order to avoid supplying discoloured water to consumers. Traditional manganese monitoring in water reservoirs consists of costly and time-consuming manual lake samplings and laboratory analysis. However, vertical profiling systems can automatically collect and remotely transfer a range of physical parameters that affect the manganese cycle. In this study, a manganese prediction model was developed, based on the profiler's historical data and weather forecasts. The model effectively forecasted seven-day ahead manganese concentrations in the epilimnion of Advancetown Lake (Queensland, Australia). The manganese forecasting model was then operationalised into an automatically updated decision support system with a user-friendly graphical interface that is easily accessible and interpretable by water treatment plant operators. The developed tool resulted in a reduction in traditional expensive monitoring while ensuring proactive water treatment management.     

A user-friendly software package for VIC hydrologic model development

The Variable Infiltration Capacity (VIC) hydrologic and river routing model simulates the water and energy fluxes that occur near the land surface and provides useful information regarding the quantity and timing of available water within a watershed system. However, despite its popularity, wider adoption is hampered by the considerable effort required to prepare model inputs and calibrate the model parameters. This study presents a user-friendly software package, named VIC-Automated Setup Toolkit (VIC-ASSIST), accessible through an intuitive MATLAB graphical user interface. VIC-ASSIST enables users to navigate the model building process through prompts and automation, with the intention to promote the use of the model for practical, educational, and research purposes. The automated processes include watershed delineation, climate and geographical input set-up, model parameter calibration, sensitivity analysis, and graphical output generation. We demonstrate the package's utilities in various case studies.     

MINLP‐based hybrid strategy for operating mode selection of TES‐backed‐up refrigeration systems

This brief deals with the satisfaction of the daily cooling demand by a hybrid system that consists of a vapor‐compression refrigeration cycle and a thermal energy storage (TES) unit, based on phase change materials. The addition of the TES tank to the original refrigeration plant allows to schedule the cooling production regardless of the instantaneous demand, given that the TES tank can store cold energy and release it whenever deemed appropriate. The scheduling problem is posed as an optimization problem based on mixed‐integer nonlinear programming (MINLP) since it includes both discrete and continuous variables. The latter corresponds to the references on the main cooling powers involved in the problem (cooling production at the evaporator and TES charging/discharging), whereas the discrete variables define the operating mode scheduling. Therefore, in addition to the hybrid features of the physical plant, a hybrid optimal control strategy is also proposed. A receding horizon approach is applied, similar to model predictive control (MPC) strategies, while economic criteria are imposed in the objective function, as well as feasibility issues. The TES state estimation is also addressed since its instantaneous charge ratio is not measurable. The proposed strategy is applied in simulation to a challenging cooling demand profile, and the main advantages of the MINLP‐based strategy over a nonlinear MPC‐based scheduling strategy previously developed are highlighted, regarding operating cost, ease of tuning, and ability to adapt to cooling demand variations.     

Output feedback control of linear fractional transformation systems subject to actuator saturation

In this paper, the control problem for a class of linear parameter varying (LPV) plant subject to actuator saturation is investigated. For the saturated LPV plant depending on the scheduling parameters in linear fractional transformation (LFT) fashion, a gain-scheduled output feedback controller in the LFT form is designed to guarantee the stability of the closed-loop LPV system and provide optimised disturbance/error attenuation performance. By using the congruent transformation, the synthesis condition is formulated as a convex optimisation problem in terms of a finite number of LMIs for which efficient optimisation techniques are available. The nonlinear inverted pendulum problem is employed to demonstrate the effectiveness of the proposed approach. Moreover, the comparison between our LPV saturated approach with an existing linear saturated method reveals the advantage of the LPV controller when handling nonlinear plants.     

Modelling plant control strategies and their applications into a knowledge-based system

The high complexity of a plant control system related structuring the domain expert knowledge into a knowledge base could decrease task. This paper presents a strategy adopted to model the application of control strategies employed in surveyed companies. Control engineering plays an important part in any industrial plant. Good control and optimisation of correct control strategies is therefore very crucial for the effective running of a control task related establishment of any kind. The control strategy approaches, must be followed right from system identification, modelling, validation, test and implementation. These stages are not always transparent when dealing with a system whose mathematical model does not exist or is difficult to obtain. When faced with this kind of problem, control enhancement techniques, such as knowledge-based, and intelligent system are always an obvious alternative.     

Networked recursive filtering for time-delayed nonlinear stochastic systems with uniform quantisation under Round-Robin protocol

In this paper, the design of networked recursive filter is investigated for a kind of nonlinear stochastic systems subject to missing measurements, fixed time-delay and uniform quantisation under the Round-Robin protocol. In a digital platform, the information coming from multi-sensors could be subject to missing measurements due to the environment effect and then need to be transformed into digital signals via uniform quantizers. In addition, the Round-Robin protocol is adopted to govern the token accessing to channel media so as to both save energy and mitigate the data congestion. A novel extended Kalman-type recursive filter is constructed that firstly combines the Round-Robin protocol, namely, in the form of the augmentation of the plant system dynamics and the protocol-induced periodic measurements. In the simultaneous consideration of system delays, missing measurements, uniform quantisation, as well as the Round-Robin protocol, the purpose for the discussed filtering problem is to obtain a set of filter parameters over a finite-horizon to minimise the upper bound of filtering error covariance as far as possible. Via elaborate mathematical analysis, the desired filter parameter is obtained by virtue of solving two Riccati-type optimisation equations, which are dependent on the latest estimation states. The genetic algorithm has been introduced to optimise the dynamic parameter selections. In addition, it is revealed in theory that the trace of the upper bound of filtering error covariance is non-decreasing as the quantisation level increases. Finally, the effectiveness of the proposed design scheme is inspected by a discretised maneuvering target tracking system.     

Operation Efficiency Optimisation Modelling and Application of Model Predictive Control

The efficiency of any energy system can be charaterised by the relevant efficiency components in terms of performance, operation, equipment and technology (POET). The overall energy efficiency of the system can be optimised by studying the POET energy efficiency components. For an existing energy system, the improvement of operation efficiency will usually be a quick win for energy efficiency. Therefore, operation efficiency improvement will be the main purpose of this paper. General procedures to establish operation efficiency optimisation models are presented. Model predictive control, a popular technique in modern control theory, is applied to solve the obtained energy models. From the case studies in water pumping systems, model predictive control will have a prosperous application in more energy efficiency problems.      

Minimizing energy consumption in reverse osmosis membrane desalination using optimization-based control

This work focuses on the design and implementation of an optimization-based control system on an experimental reverse osmosis (RO) membrane water desalination process in order to facilitate system operation at energy optimal conditions. A nonlinear model for the RO process is derived using first principles and the model parameters are computed from experimental data. This model is combined with appropriate equations for reverse osmosis system energy analysis to form the basis for the design of a nonlinear optimization-based control system. The proposed control system is implemented on UCLA’s experimental RO desalination system and its energy optimization capabilities are evaluated.     

An iterative approach to the optimal co-design of linear control systems

This paper investigates the optimal co-design of both physical plants and control policies for a class of continuous-time linear control systems. The optimal co-design of a specific linear control system is commonly formulated as a nonlinear non-convex optimisation problem (NNOP), and solved by using iterative techniques, where the plant parameters and the control policy are updated iteratively and alternately. This paper proposes a novel iterative approach to solve the NNOP, where the plant parameters are updated by solving a standard semi-definite programming problem, with non-convexity no longer involved. The proposed system design is generally less conservative in terms of the system performance compared to the conventional system-equivalence-based design, albeit the range of applicability is slightly reduced. A practical optimisation algorithm is proposed to compute a sub-optimal solution ensuring the system stability, and the convergence of the algorithm is established. The effectiveness of the proposed algorithm is illustrated by its application to the optimal co-design of a physical load positioning system.