Robustness evaluation of different controllers in the presence of flow rate variations

This paper proposes a direct approach to evaluate achievable robustness to flow rate variations of different controllers (PID and advanced algorithms), for typical process dynamics (First Order Plus Time Delay with different time delay/lag ratio), able to represent very common heat exchange equipment. Starting from comparable nominal performance, the effect of flow rate variations on process parameters and consequently on achievable performance is analyzed in simulation up to the onset of marginal stability conditions. Flow rate variations act as “structured” uncertainty on parameters and the proposed procedure is able to indicate maximum allowable variations in a more realistic and efficient way, avoiding the conservatism implicit in most of analytical design techniques available in the literature. The proposed technique evaluates in a straightforward way the Maximum Allowed Changes (MAC) in flow rate. As inverse proportionality between flow rate and process parameters is present, also the efficacy of adopting equal percentage (EP) valves, which allow a local compensation of process gain nonlinearity, is investigated

Numerical investigation of the mixing of highly viscous liquids with Cowles impellers

This work is aimed at investigating the mixing process of highly viscous paints, used to colour leathers in the tanning industry, through Computational Fluid Dynamics (CFD). In particular, a mixing tank is fed with a master liquid and different liquid pigments and then stirred by Cowles impellers in order to obtain a paint of a uniform colour. The typical dynamic viscosity of the liquids in this process is μ ~ O(0.1-10) Pa·s, while the Cowles rotational speed is usually very high, i.e. 3000-5000 rpm. The numerical model is based on the solution of the unsteady Reynolds-Averaged Navier–Stokes (RANS) equations for continuity, momentum and species mass fractions, the latter being used to describe the different components. The impeller motion is modelled through the Sliding Deforming Mesh (SDM) approach, using rotating (unstructured) meshes in the impeller region and a static (structured) mesh in the remainder of the tank. The master liquid and coloured pigments are assumed to stratify within the tank at initial time and the steady rotational speed is then imposed abruptly to the impellers. The level of homogeneity in the stirred tank is evaluated through the analysis of component concentration fields over time. In particular, such local concentrations can be used to determine the mixture colour in different regions of the tank, and hence predict the degree of homogeneity at different times; this is accomplished by defining a proper homogeneity indicator based on the spatial variance of the estimated colour. The proposed numerical model provides an efficient method to investigate the colour of the mixture and to evaluate an appropriate mixing time. The methodology gives also important indications for the tank design, especially useful in the case of non-conventional impellers, high rotation rates and viscous fluids

An automatic system for modeling and controlling color quality of dyed leathers in tanneries

This paper presents an automatic system for modeling and controlling color quality of dyed leathers of an Italian tannery. The proposed software is implemented within the IT company system, and is fully integrated with the machineries of the finishing line, that is, a spraying cabin with a robotic carousel, and an electronic tintometer system. Suitable experimental tests according to the Design of Experiments (DoE) are firstly defined, executed and analyzed for a series of color tones of interest. In order to derive and validate a set of colorimetric models able to evaluate and predict the color rendering of painted leathers, a set of recipes of basic dye pigments and data of light reflection measured by a multispectral camera are used. Principal Component Analysis is applied for dimensionality reduction, and linear least squares regression is employed to identify these data-driven models, which are then used for color control purpose. A color correction feedback strategy is indeed developed in order to converge towards the various target formulations. The control algorithm aims at reaching the multispectral reading values of the reference, that is, the first sample of unknown color recipe starting from the most similar archive base and appropriately updating the recipe of pigments, by using the measurement of leather samples prepared from time to time by the finishing line machineries. A set of company data are used to successfully validate the identified colorimetric models and the proposed color correction strategy

Data-driven Models for Advanced Control of Acid Gas Treatment in Waste-to-energy Plants

This paper presents a study of identification and validation of data-driven models for the description of the acid gas treatment process, a key step of flue gas cleaning in waste-to-energy plants. The acid gas removal line of an Italian plant, based on the injection of hydrated lime, Ca(OH)2, for the abatement of hydrogen chloride, HCl, is investigated. The final goal is to minimize the feed rate of reactant needed to achieve the required HCl removal performance, also reducing as a consequence the production of solid process residues. Process data are collected during dedicated plant tests carried out by imposing Generalized Binary Noise (GBN) sequences to the flow rate of Ca(OH)2. Various input-output and state-space models are identified with success, and related model orders are optimized. The models are then validated on different datasets of routine plant operation. The proposed modeling approach appears reliable and promising for control purposes, once implemented into advanced model-based control structures

Optimally Managing Chemical Plant Operations: An Example Oriented by Industry 4.0 Paradigms

Updating industrial facilities to increase the level of automation and digitalization to match Industry 4.0 paradigms has become essential for many companies. Following such a trend, this paper presents a real-time optimization algorithm that plays a central role in a larger project framework devoted to highly interconnecting different network components of an Italian chemical industrial site. The proposed methodology aims at best managing the production rates of various products to fulfill a sales plan organized to satisfy numerous client requests. The considered model takes into account both batch and continuous processes as well as salable and non-storable products. The algorithm structure relies on the use of a non-linear optimization scheme and on the concepts of batch scheduling. Different features of the proposed methodology have been tested on real plant data, showing how the predicted forecast always improved the initial operation plan by considering both aspects of feasibility and economic nature. The use of the proposed algorithm assures the basis for fully integrating the control systems and the selling department of the facility in a more interactive and responsive manner

A Performance Monitoring Tool to Quantify Valve Stiction In Control Loops

The paper presents main features of a performance monitoring system, which allows stiction quantification. It implements a methodology which permits one to reproduce the unknown stem position, without requiring any additional knowledge, based only on data normally registered in industrial plants (controller output OP, controlled variable PV and set point SP). A general procedure is proposed to discard data for which quantification is very likely to give wrong indications and to restrict the application to appropriate cases. Simulations show that several sources of perturbations can be eliminated; on the contrary, the presence of external disturbances may alter the reliability of stiction evaluation and then stiction diagnosis techniques must be applied firstly. Results are confirmed by application to industrial valves for repeated acquisitions, in the framework of a performance monitoring system implemented for continuous supervision of control loops and valve maintenance scheduling and checking

Enhancing sustainability of acid gas treatment in a Waste-to-Energy plant via Model Predictive Control

The municipal solid waste fed to waste-to-energy (WtE) plants produces acid flue gas with a wide and volatile range of composition, a scenario which implies significant process disturbances for traditional control schemes. In this paper, a two-stage dry sorbent system in an Italian treatment plant is used as a case study. The focus was on the first stage of abatement, where calcium hydroxide is injected into a reactor to reduce the hydrogen chloride (HCl) content in the acid flue gas. A state-space model, identified and tested in a previous work, was here implemented in a model predictive control (MPC) structure. In particular, four different MPC solutions were derived and tested on routine data in order to increase the process performance. Three suitable key performance indicators were used to assess the quantitative comparison between the proposed advanced control approaches and the simulated architecture of the one implemented in the WtE plant. The analysis evidenced the benefits obtained by MPC structures with respect to the current sub-optimal control architecture. Controller tuning and the possibility to include the online HCl concentration in the model have been also tested to highlight the best solution. Overall, the advanced controllers allowed one to achieve a solid profit-safety trade-off, by strictly tracking set-point on outlet HCl concentration and, at the same time, by requiring minimal sorbent doses and then minimal solid residues, with limited stress on the actuation system

Enhancing MPC formulations by identification and estimation of valve stiction

A common source of poor control performance in industrial processes is represented by stiction in control valves, which often induces offset, oscillating behavior, and even loss of stability. Recent studies have investigated the effectiveness of embedding stiction models into model predictive controller (MPC) schemes, moving from stiction unaware to different stiction aware formulations, which help to remove fluctuations and may guarantee higher set-point tracking ability. To this aim, along with the process model the controller needs to use a dynamic model of sticky valves. This paper proposes an efficient, computational approach to obtain both valve and process dynamics, under the framework of Hammerstein system identification, which is based on nonlinear, gradient-based, numerical optimization. In order to improve the computational behavior and effectiveness of the methodology, a recently proposed smoothed model of stiction is deployed. The proposed methodology is validated in several (single-input single-output, and multivariable) examples, where the effectiveness of the obtained stiction aware MPC regulator is also evaluated against a stiction unaware counterpart

Data driven performance monitoring and retuning using PID controllers

This paper presents a joint methodology for performance assessment and retuning of PID controllers. The methods are data-driven and based on the set-point response collected during operation or tests, using as reference input steps, ramps, and other signals. For each set-point response, the IAE (Integral of Absolute Error) is computed and a statistical test is evaluated to check if performance is below a given threshold. A data-driven design method is applied if new controller gains are required to resume the performance, using closed-loop data and a reference model obtained also from data under operation with good performance. Both monitoring and design require only closed-loop data, and statistical tests are used to provide evidence that monitoring and retuning were successful. Three applications for two different pilot plants are presented, evaluating: (i) flow, level, and pressure loops, (ii) outer and inner loops, (iii) validation of closed-loop data, (iv) performance recovery after introducing faults that reduce performance, (v) performance monitoring and retuning for three levels of performance. As illustrated, the proposed methodology can be applied directly to closed loops even in the presence of very noisy signals, with no assumptions about the design method used for the controller design

Industrial PID control loop data repository and comparison of fault detection methods

This paper presents the control loop data of industrial controllers that are recently made available online. All data is confirmed and some of it has been published previously to develop fault detection and diagnosis methods. Methods to detect faults that occur during the operation of an industrial process are important and have attracted attention previously but are not always widely used in industry. One of the reasons is that any method needs to be robust and fully automated. The purpose of the data repository is to present data to test methods so that false positives and negatives are reduced to an insignificant number. Three previously published methods – oscillation detection based on the autocorrelation function, the idle index and a method for quantization detection – together with a simple, novel saturation detection method and one new detection methods are applied to all industrial data. The results are discussed and ways to improve the robustness and automation potential of these methods