Sebastião Feyo de Azevedo

Adaptive Recurrent Neural network control of biological wastewater treatment

Three adaptive neural network control structures to regulate a biological wastewater treatment process are introduced: indirect, inverse model, and direct adaptive neural control. The objective is to keep the concentration of the recycled biomass proportional to the influent flow rate in the presence of periodically acting disturbances, process parameter variations, and measurement noise. This is achieved by the so‐called Jordan Canonical Recurrent Trainable Neural Network, which is a completely parallel and parametric neural structure, permitting the use of the obtained parameters, during the learning phase, directly for control system design. Comparative simulation results confirmed the applicability of the proposed control schemes.

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Abstract Hybrid semi-parametric models consist of model structures that combine parametric and nonparametric submodels based on different knowledge sources. The development of a hybrid semi-parametric model can offer several advantages over traditional mechanistic or data-driven modeling, as reviewed in this paper. These advantages, such as broader knowledge base, transparency of the modeling approach and cost-effective model development, have been widely recognized, not only in academia but also in the industry. In this paper, the most common hybrid semi-parametric modeling and parameter identification techniques are revisited. Applications in the areas of (bio)chemical engineering for process monitoring, control, optimization, scale-up and model-reduction are reviewed. It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.

Databank transfer-of-information, shortcut and exact estimators used in the wastewater biological treatment process identification

Abstract The biological treatment is the most complex step in removing organic and inorganic pollutants from wastewaters, being very sensitive to input-flow oscillations, operating conditions, and biomass evolution. Sudden increases in substrate concentration or some inhibitory substances, deterioration of the biomass, or few observed species, all of these lead to a difficult process modelling and need replaced biokinetics identification for each waste and biomass type. However, the bioprocess numerical analysis is crucial for obtaining significant improvements in the wastewater treatment (WWT) plant performances and safety indices even under imperfect data. The paper exemplifies an advanced route to quickly on-line identify the biodegradation characteristics of new substrates processed by a series of perfectly mixed aeration basins with biomass recycle. The Monod kinetics is recursively identified by using the available collection of plant previous transient operating data and a robust shortcut estimator (MIP). The approximate solution is periodically refined with an exact nonlinear estimator (NLS), checked for consistency and significance versus prior information, and stored in databanks.

Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

BackgroundThis paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics.ResultsThe proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems.ConclusionsSignificantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.

Artificial neural networks for modeling in reaction process systems

This work is focused on developing a more efficient computational scheme for estimation of process reaction rates based on NN models. Two scenarios are considered: (1) the kinetics coefficients of the process are completely known and the process states are partly known (measured); (2) the kinetics coefficients and the states of the process are partly known. The contribution of the paper is twofold. From one side we formulate a hybrid (ANN and mechanistic) model that outperforms the traditional reaction rate estimation approaches. From other side, a new procedure for NN supervised training is proposed when target outputs are not available. The two scenarios are successfully tested for two benchmark problems, estimation of the precipitation rate of calcium phosphate and estimation of sugar crystallization growth rate.

SUGAR ANALYSIS OF INDUSTRIAL PENICILLIN FERMENTATION BROTHS CONTAINING COMPLEX NUTRIENT SOURCES

Four methods for sugar analysis in industrial penicillin-G fermentation broths are compared. It is concluded that volumetric and colorimetric assays have little value in specific analyses of complex industrial fermentation media. The combined use of HPLC and enzymatic methods constitutes an efficient and simple approach to the problem of mono-and polisaccharide on-line monitoring in the fermentation process.

Model Predictive Control Strategies for Batch Sugar Crystallization Process

The industrial processes are governed generally by general principles of the physics and chemistry. With the aid of data acquisition systems supported in microprocessor it is possible to obtain real data of the industrial process, that it characterizes in detail his dynamics and input-output dependency. Several methods of identification allow, from these data, to obtain linear and nonlinear models of these processes (Rossiter, 2003; Morari, 1994); which are the base to predict the process behaviour within all the family of the model based predictive controllers (MPC). Diverse algorithms MPC have demonstrated its effectiveness in those control loops characterized by strong nonlinearities, difficult dynamic, inverse answers and great delay; that they are generally those of greater influence in the final product quality and the process efficiency (Allgower et al., 2004; Qin & Badgwell 2003). One of the most important steps in the implementation of a MPC is just the obtaining of the model that can predict with reliability the future behaviour of the controlled variable, like answer to a predefined optimized control action (Rawlings 2000). This work applies two kind of MPC: (i) Classical Model-Based Predictive Control and (ii) Neural Network Model Predictive Control (NNMPC). The classical MPC strategy uses a discrete model obtained from general phenomenological model of the feed-batch crystallization process, consisting of mass, energy and population balance. The NNMPC strategy uses to obtain a neural network, the training algorithms proposed in the Neural Network Toolbox of MatLab (version 7.04) (Bemporad et al., 2005). In this particular case it is analyzed a fed-batch sugar crystallization process, in this process there is abundant information, detailed mathematical models and real industrial data. (Chorao, 1995; Feyo de Azevedo & Goncalves 1988; Georgieva et al., 2003). This fact motivated the use of the neural networks to model the process and to propose a neural network MPC (NNMPC) that considers the process like a gray box, of which has inputoutput information and the historical experience of he process behaviour.

Novel Computational Methods for Modeling and Control in Chemical and Biochemical Process Systems

This chapter is focused on developing more efficient computational schemes for modeling and control of chemical and biochemical process systems. In the first part of the chapter a theoretical framework for estimation of general process kinetic rates based on Artificial Neural Network (ANN) models is introduced. Two scenarios are considered: i) Partly known (measured) process states and completely known kinetic parameters; ii) Partly known process states and kinetic parameters. The two scenarios are successfully tested for two benchmark problems, estimation of the precipitation rate of calcium phosphate and estimation of sugar crystallization growth rate. In the second part of the chapter the developed ANN-based models are integrated into the structure of a nonlinear model predictive control (MPC). The proposed ANN-MPC control scheme is a promising framework when the process is strongly nonlinear and input-output data is the only process information available.

Simulation of multiple-effect evaporators

Abstract A PC based simulator of the steady-state behaviour of multiple-effect calandria (or equivalent) evaporators is presented. The package, ‘MULTEVA’ can be applied in studies concerning thermodynamic design, in the analysis of alternative forms of operation and in the monitoring of unit efficiency. The latter is achieved through an option which performs the estimation of the heat transfer coefficients for given operation data. Three types of feed configurations are allowed, viz. - forward, mixed and backward. Up to 20 effects are accepted for analysis in each configuration. A database on the relevant thermodynamical and physical properties of the solute, solvent and solution has been created with read/write capacities. The simulator derives from the database all the necessary information to carry out the computations. The general model employed is based on theoretical heat and mass balances and on equilibrium considerations. It has already proved its efficiency in industrial applications of sugar liquor evaporation.

Intelligent Predictive Control - Application to Scheduled Crystallization Processes

The purpose of this paper is twofold. On one hand, we propose a modification of the general Model Predictive Control (MPC) approach where a prespecified tracking error is tolerated. The introduction of error tolerance (ET) in the MPC optimization algorithm reduces considerably the average duration of each optimization step and makes the MPC computationally more efficient and attractive for industrial applications. On the other hand a challenging scheduled crystallization process serves as a case study to show the practical relevance of the new intelligent predictive control. Comparative tests with different control policies are performed: i) Classical MPC with analytical or Artificial Neural Network (ANN) process model; ii) ET MPC with analytical or ANN process model; iii) Proportional-Integral (PI) control. Besides the computational benefits of ET MPC, the integration of ANN into the ET MPC brings substantial improvements of the final process performance measures and further relaxes the computational demands.