Optimisation of the anaerobic digestion of agricultural resources.

https://repositorio-aberto.up.pt/bitstream/10216/103038/3/68419.1.pdf

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

This paper presents the synthesis and analysis of optimal tuning of proportional integral derivative (PID) parameters for different process systems: first order plus time delay (FOPTD), second order plus time delay (SOPTD) and second order plus time delay with lead (SOPTDLD). This work involved optimization of the PID control parameters to achieve the minimization of the integral absolute error (IAE). A set of new and generalized tuning correlations relating the controller parameters to the process parameters was obtained for step changes in set point and load separately. Simulation results showed that the use of the proposed tuning correlations results in superior closed loop performance compared to other tuning techniques previously proposed in the literature.

Optimal tuning of PID controllers for FOPTD, SOPTD and SOPTD with lead processes

Artificial neural networks (ANN) provide a range of powerful new techniques for solving problems in sensor data analysis, fault detection, process identification, and control and have been used in a diverse range of chemical engineering applications. This paper aims to provide a comprehensive review of various ANN applications within the field of chemical engineering (CE). It deals with the significant aspects of ANN (architecture, methods of developing and training, and modeling strategies) in correlation with various types of applications. A systematic classification scheme is also presented, which uncovers, classifies, and interprets the existing works related to the ANN methodologies and applications within the CE domain. Based on this scheme, 717 scholarly papers from 169 journals are categorized into specific application areas and general (other) applications, including the follo wing topics: petrochemicals, oil and gas industry, biotechnology, cellular industry, environment, health and safety, fuel and energy, mineral industry, nanotechnology, pharmaceutical industry, and polymer industry. It is hoped that this paper will serve as a comprehensive state-of-theart reference for chemical engineers besides highlighting the potential applications of ANN in CE-related problems and consequently enhancing the future ANN research in CE field.

Artificial neural networks: applications in chemical engineering

The reaction of alpha,beta-unsaturated ketoximes with fluoroalkanosulfonyl fluorides in the presence of 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) underwent the Beckmann rearrangement smoothly to afford the corresponding acid-sensitive enamides in moderate to excellent yields, which provides a new efficient method for the preparation of acid-sensitive enamides. (C) 2014 Elsevier Ltd. All rights reserved.

Cascade control of superheated steam temperature with neuro-PID controller.

http://paginas.fe.up.pt/~sfeyo/Docs_SFA_Publica_Conferences/SFA_JP_20040101_CCEng_Alois.pdf

On-line tuning of a neural PID controller based on plant hybrid modeling

In order to monitor and control bioprocesses, online measurements of certain biological variables, like biomass concentration, are normally required. Unfortunately, there is a lack of highly reliable and low cost instruments for online measuring these important variables. These limitations have motivated the use of observers. This paper describes a state observer for bioprocesses based on a hybrid fuzzy model. The model combines the mass balances and a fuzzy inference system for approximating the uncertain dynamic of the kinetic reactions. The convergence, stability, and robustness of the observer are analyzed. The main features of this observer are illustrated by considering a lactic fermentation.

An adaptive fuzzy hybrid state observer for bioprocesses

A model-based optimization of an industrial fed-batch sugar crystallisation process is considered in this paper. The objective is to define the optimal profiles of the manipulated process inputs, the feeding rate of liquor/syrup and the steam supply rate, such that the crystal content and the crystal size distribution (CSD) measures at the end of the batch cycle reach the reference values. A knowledge-based hybrid model is implemented, which combines a partial first principles model reflecting the mass, energy and population balances with an artificial neural network (ANN) to estimate the kinetics parameters - particle growth rate, nucleation rate and the agglomeration kernel. The simulation results demonstrate that the very tight and conflicting end-point objectives are simultaneously feasible in the presence of hard process constrains.

Dynamic Optimisation of Industrial Sugar Crystallization Process based on a Hybrid (mechanistic+ANN) Model

This work deals with the fusion of the data-based and analytical submodels in the process engineering. In contrast to the traditional way of process reaction rates identification by an exhaustive and/or expensive search for the most appropriate parameterized structure, a neural network (NN) based procedure is developed here to identify the reaction rates in the framework of a first principles process model. Since the reaction rates are not measured variables a particular network training structure and algorithm are developed to make possible the supervised NN learning. Our contribution is focused on the general modeling of a class of nonlinear systems representing several industrial processes including crystallization and precipitation, polymerization reactors, distillation columns, biochemical fermentation and biological systems. The proposed algorithm is further applied for estimation of the precipitation rate of calcium phosphate and compared with alternative solutions.

https://paginas.fe.up.pt/~sfeyo/Docs_SFA_Publica_Conferences/SFA_CP_20070812_IJCNN_PG.pdf

S.F. de Azevedo

Papers

On-line tuning of a neural PID controller based on plant hybrid modeling

An adaptive fuzzy hybrid state observer for bioprocesses

Dynamic Optimisation of Industrial Sugar Crystallization Process based on a Hybrid (mechanistic+ANN) Model

Process modeling strategy combining analytical and data based techniques - I. NN identification of reaction rates with known kinetics coefficients

Determination of the physiologic parameters, thermal gradient and index of heat tolerance in different breeds of goats.