Model-based process analysis, control, monitoring and

Model types

As it was already mentioned, dierent sources of information lead to dierent knowledge about the process thus dierently applicable models can be devel-oped (see Figure 1.4). Obviously the accuracy (performance), interpretability or generality attributes of the achieved model highly depend on the information source as well.

In chemical engineering practice, the experimentally measured process data based models are also called a posteriori (or black box) models where model structure is unknown, while mechanistic knowledge based models are called a priori (or white box) models. The application of fuzzy logic models is a novel approach developed in the past decade, which rapidly spreads in process analysis and control area [10]. Semi-mechanistic (gray or hybrid) models are achieved by mixing a posteriori model elements with a priori model parts in order to ll the gaps of unknown phenomena in the model. The 'whiteness' of the model depends on the ratio between the two model types. Moreover,

PROCESS Fuzzy Logic

models

Empirical models

First principle models Mechanistic knowledge

Expert knowledge Measured data

Figure 1.4: Attainable process models based on dierent information sources.

application of hybrid models can be a bridge between scales in multi-scale models.

Regarding time domain, the continuous time models are usually closer to physical considerations, whereas the discrete-time system behavior is consid-ered to be dened at a sequence of time instants related to measurements. The discrete-time models are closely related to implementation problems of digital processing.

Process models in control

Among others, process control is also an inevitable area in chemical engineer-ing. It also helps the continuous development of currently operating or upcom-ing technologies through drivupcom-ing the process in its predominantly optimized way of production.

The most simple control strategy where process models play an important role is PID-controller based control. This strategy can be applied eectively on controlling a single variable without extensive process knowledge for pro-cesses with dierent dynamics [11]. For a complex system, it means that each control loop needs to be considered independent and PID controllers are tuned individually. This can be done by using a reduced SISO interpretation of the process model or all other model inputs are kept constant in the original model.

Additionally, there are techniques to tune PID controllers at once parallel for dependent control loops in MIMO systems as well [12].

PID controllers have many interpretations and are applied at basic con-trol level of the DCS systems [13]. Nevertheless the implemented basic PID

algorithms do not have an explicit built-in process model for control, only sim-plied models are identied for parameter tuning of the controller. Several tuning techniques were developed, like Ziegler-Nichols [14], Cohen-Coon [15], direct synthesis method [16], which use a rst order plus time delay (FOPTD) representation of the system to be controlled. As many distributed param-eter systems cannot be modelled via FOPTD models, new techniques were developed for second order and second order plus lead models as well [17].

In the following, some advanced model-based control strategies are pre-sented, which explicitly utilize a model of the process:

- Internal model control (IMC). The IMC structure consists of a controller, a process model and a process. In an internal model control arrangement, the process model is placed parallel with the real system. The dierence between the system and model output represents the modeling error plus un-modeled process disturbances. This dierence is then fed back into the controller where it is used to compensate disturbance and eects of the modeling error. Detailed description can be found in [18, 19].

- Adaptive control. To be able to follow up time-variant parameter changes of a system, adaptive control strategy can be applied. This strategy utilizes the process model for parameter identication at every sample period and the adaptation mechanism converts the estimated system parameter changes into new controller design [20].

- Model-based robust control. Robust control of systems is based on the H-innity (H_∞) control theory, which characterizes the control problem as an optimization problem for controller design of robust performance or robust stabilization. Drawbacks of the technique are the necessity of extended mathematical understanding and good model basis. For this later task, Linear Time-Invariant (LTI) or Linear Parameter-Varying (LPV) models are applied in the literature (e.g. [21, 22] and [23, 24]).

The LPV model is essentially a parameterized family of LTI models. LPV models are advantageous in the sense that they guarantee stabilization for the whole operating regime, large transients in switching are avoided and only one controller needs to be designed (instead of scheduling several LTI based controllers), but their performance can be poor. A comparison of the two model approaches can be found in [25].

- Model predictive control (MPC). MPC refers to a class of control algo-rithms, which utilize an explicit process model to predict future responses of a process. At each control interval an MPC algorithm attempts to optimize future process behavior by computing a sequence of future ma-nipulated variable adjustments. The rst input in the optimal sequence is then sent into the process, and the entire calculation is repeated at subsequent control intervals [26].

Based on these basic control strategies, several combined model-based con-trol mechanisms have been developed, like adaptive internal model concon-trol [27], adaptive internal fuzzy model control [28] or linearized IMC-MPC strat-egy [29].

From all the above mentioned strategies, MPC is the most widely applied as part of advanced process control systems while using simple PID algorithms at local control level. A survey of these can be found in [30] with numer-ous citations regarding the evolution of MPC. Currently, there are several industrial advanced process control packages on the market which apply the multi-variable model predictive control technology, like Prot Controller from Honeywell Inc. or APC Suite from Yokogawa Inc. The application of such advanced process control technologies denitely enables pushing the process to a higher level of productivity and delivering bottom line improvement.

Process models in analysis and optimization

Additionally to the crucial role of process models in control, process models are widely applicable for various purposes. Without trying to collect all of them, major areas are listed in the following with some citations as typical examples from thousands of related publications:

- Prediction or forecasting can serve to obtain better knowledge of the process, to verify theoretical models, to predict new phenomena, etc.

[31, 32]. In this context, it is important to represent external actions and external disturbances and to use knowledge of statistical characteristics of random variables, as there is usually little theoretical or practical possibility of determining such characteristics in advance.

- Control system analysis and design provide a rich eld for the application of modeling and identication [33, 34, 35], since application of models

allows quantitative predictions to be made concerning crucial features of control systems such as stability conditions and the development of oscillatory behavior.

- Models often serve as the basis of monitoring or supervision, error/fault detection and isolation and process diagnosis in large systems [36, 37, 38, 39].

- Model based alarm management/ltering is an area close to fault detec-tion. Alarm systems help operators to correct dangerous situations thus avoid emergency shutdown system to intervene [40, 41, 42].

- Industrial processes in continuous operation require system optimization for their economy, which in turn requires very accurate modeling [43, 44].

- Soft sensors can provide information on process variables, which are di-rectly cannot be measured or their measurement is troublesome or too costly. Usually, these variables can be computed from other measure-ments based on suitable models [45, 46, 47].

- Simulation based on mathematical models is widely used for the assess-ment of model complexity, for engineering design or for operator training, all of which require adequate modeling and adequate input [48].

- As a specic application of simulation purposes, operator training sys-tems have been developed to train novice operators and to enhance con-trol and maintenance abilities of operators for standard procedures and emergency operation. In this way, it is able to bridge the gap between required skills and technical experience [49]. Mostly, these advanced dy-namic model based simulator systems include replica operator stations connected to instructor PC in order to closely emulate reality and eval-uate results [50, 51].

- As systems have become more complex, operators' responsibility and role in decision making turned into a highly important requirement, for which operator support systems were developed. "An Operator Support Sys-tem (OSS) is a combination of information sysSys-tems, meeting structures, mathematical models and education/training programs, aimed at the support, development and improvement of the process operation"[52].

These various tools guide the operators in decision making to run the technology more optimally in an integrated way [53, 54]: all the previ-ously mentioned model-based techniques (and the data-based techniques detailed in Section 1.2) can serve as parts of an operator decision support system. For OSS applications, several solutions and such elements can be found in the literature like in [55, 56, 57].

Modeling and simulation products on the market

Due to the extraordinary growth in information technology in the past decades, numerous softwares and software packages came into existence for various pur-poses and based on dierent programming languages to help modeling and sim-ulation of chemical engineering processes. They are applied in design as well as in operation of existing plants for process optimization, units troubleshoot-ing or de-bottlenecktroubleshoot-ing, plants revamptroubleshoot-ing or performtroubleshoot-ing front-end engineertroubleshoot-ing analysis. Only the major ones are listed here, without aiming at completeness:

- ProSimPlus (ProSim), a simulator environment for wide range of steady-state industrial processes.

- PRO/II (SimSci-Esscor), steady-state simulator for oil and gas industry integrable with BATCHFRAC by Koch-Glitsch for batch process simu-lation and extendable with interface to MS Excel (SIM4ME).

- Aspen ONE (AspenTech Inc.), a complete software package containing steady-state and dynamic process simulators for chemical industry (As-pen Plus and As(As-pen Dynamics), and steady-state simulators for batch processes (Aspen Batch Plus).

- HYSYS (formerly Hyprotech Ltd., then AspenTech and Honeywell Inc.), part of the Aspen ONE package steady-state and dynamic process sim-ulators for oil and gas industry (Aspen HYSYS and HYSYS Dynamics).

- UniSim family (Honeywell Ltd.) consists of four elements (steady-state design, operations, optimization and dynamic simulation packages), which are connected in the so called Unisim simulation life-cycle.

- ChemCAD (Chemstations Inc.), similarly to Aspen ONE, this pack-age provides solutions to steady-state Steady State), dynamic

Dynamic) and batch Batch) simulation, heat exchanger design (CC-Therm) or data reconciliation/parameter estimation (CC-Recon).

- Simulink (The Mathworks Inc.), a general purpose dynamic simulator, which integrates into the Mathwork's MATLAB environment, to be able to achieve overall functionality in all areas (modeling, identication, anal-ysis, control).

- gPROMS Product Family (Process Systems Enterprise Ltd.). gPROMS ModelBuilder is applicable for steady-state and dynamic simulation as well as optimization, while gPROMS Objects creates interfaces to several other, widely used softwares, like MATLAB (gO:MATLAB), Simulink (gO:Simulink), computational uid dynamics (gO:CFD) or Aspen or PRO/II (gO:CAPE-OPEN) in order to utilize both softwares' advan-tages.

In these software environments, common chemical engineering unit models and thermodynamic methods are built in and are easily connectible through a graphical screen thus designing a process owsheet is very user-friendly and fast. Model parameters are taken from the software's own data library or can be set by model identication functions.

The main disadvantage of these softwares is that they have the function-ality only for what is built in. At this point, Simulink as a general purpose simulator based on general building blocks gets a real advantage, which be-comes extraordinary if one integrates the Simulink models with other MAT-LAB packages, like Data analysis, Statistics or System Identication toolboxes.

These attributes turn MATLAB and Simulink into the most widely applicable prototype developer and scientic programming tool on the market.

Another direction on the market - as a tangible sign of aiming at integrality - is that control system provider companies already realized the potential in simulation softwares: in 2004, Honeywell Inc. acquired the intellectual rights of HYSYS simulation software from Aspen Technologies Inc. in order to extend the capabilities of its Process Knowledge System (Experion PKS) and such to extend the operator training business applicability and protability [58].