1,720,993 research outputs found
Control of the Biodegradation of Mixed Wastes in a Continuous Bioreactor by a Type-2 Fuzzy Logic Controller
No full textThe paper describes the application of type-2 fuzzy logic control to a nonlinear system with bifurcations. A type-2 fuzzy logic controller is tested by simulation on a bioreactor with cell recycle that presents bifurcations. The process involves a pure culture of Pseudomonas putida containing phenol and glucose as carbon and energy sources and is characterized by two saddle-node bifurcations. Because of the bifurcational behaviour the system may become unstable also for further variation of process parameters. The simulation results show the validity of the proposed controller, that, compared with other controllers, has a higher performance in terms of robustness and response speed
Adaptive Type-2 Fuzzy Logic Control of a Bioreactor
Get PDFTwo adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and
compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the
growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity
and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive
controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the
ability of predictive control to predict future plant performance making use of a neural network model
of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI
controller, where the output scaling factor is adjusted online by fuzzy rules according to the current
trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used
as reference
Evolution of wastewater purification: a historical perspective on innovation, regulation and knowledge transfer
No full textType-2 Fuzzy Control of a Bioreactor
Get PDFAbstract—In this paper the control of a bioprocess using
an adaptive type-2 fuzzy logic controller is proposed.
The process is concerned with the aerobic alcoholic
fermentation for the growth of Saccharomyces Cerevisiae
a n d i s characterized by nonlinearity and parameter
uncertainty. Three type-2 fuzzy controllers heve been
developed and tested by simulation: a simple type-2
fuzzy logic controller with 49 rules; a type-2 fuzzyneuro-
predictive controller (T2FNPC); a t y p e -2 selftuning
fuzzy controller ( T2STFC). The T2FNPC
combines the capability of the type-2 fuzzy logic to
handle uncertainties, with the ability of predictive
control to predict future plant performance making use
of a neural network model of the non linear system. In
the T2STFC the output scaling factor is adjusted on-line
by fuzzy rules according to the current trend of the
controlled process. T h e advantage of the proposed
adaptive algorithms is to greatly decrease the number of
rules needed for the control reducing the computational
load and at same time assuring a robust control
Adaptive Type-2 Fuzzy Control of Non-linear Systems
No full textThe paper describes the development of two different
type-2 adaptive fuzzy logic controllers and their use for the
control of a non linear system that is characterized by the
presence of bifurcations and parameter uncertainty.
Although a type-2 fuzzy logic controller is able to handle the non
linearities and the uncertainties present in a system, its
robustness and effectiveness can be increased by the use of an
opportune adaptive algorithm. A simulation study was conducted
to compare the behavior of adaptive controllers with that of
simple type-1 and type-2 fuzzy logic controllers. The system to be
controlled, used for the simulation, is a continuous bioreactor for
the treatment of mixed wastes in which a culture of Pseudomonas
Putida is carried out while phenol and glucose are carbon and
energy sources. From simulations results it can be seen that both
adaptive controllers, but in particular the self tuning controller,
have a better performance being able to eliminate oscillations
that are present with basic fuzzy controllers
Application of fuzzy control to residential cogeneration with renewable energy sources
No full textA fuzzy PI controller and a conventional PI controller were adopted to develop control for a residential cogeneration system made by a biomass-fired and solar-powered fluidized bed prototype. Its mathematical model is characterized by nonlinearities and, more important, by uncertainty and variability in parameters. The paper describes in detail the PI fuzzy controller, the development of which was based on the knowledge of the continuity diagrams of the process model. Then, the paper reports a comparison in simulation between the PI fuzzy controller and the conventional PI one. The PI fuzzy controller exhibits a superior performance, as far as both robustness and rate of response. As a result, the adoption of a PI fuzzy controller turns out the best choice for this residential cogeneration system and a favorable option for nonlinear processes with uncertain or time-varying parameters
Neural Network Fuzzy Predictive Control for Penicillin Production from Biomass
Get PDFThis work proposes, through simulations in the Matlab/Simulink software environment, a neural network predictive adaptive fuzzy control (NNPAFC) of a penicillin production process taking place in a batch-fed reactor. The results of such an implementation are presented and discussed. The outcomes of the simulations under realistic process control conditions confirm that this control strategy is, more than the others, a suitable strategy for the production of penicillin. This will ensure the production of high-quality penicillin and, at the same time, guarantee high production rates, maximizing penicillin yield, and minimizing waste of raw materials and production time
Addressing Challenge and Upcycle of Biomasses in Wastewater Sludges
Get PDFIn the context of the management of wastewater purification plants, one of the major critical issues in the last five years has been, and continues to be, the unavailability of sites where the ever-increasing quantities of sludge produced can be disposed of. Within the perspective of minimizing and managing sludge, exploring enhanced solutions like syngas, biochar, and energy production is feasible. However, it is essential to shift the focus upstream and address the issue systematically by intervening appropriately in various sections of wastewater treatment processes. This work addresses the problem of minimizing the sludge produced in purification plants, with an interdisciplinary functional approach. An innovative decision-making digital tool has been developed that integrates the "Data-Driven" Digitalization approach with the "Knowledge Embedded" multidisciplinary one. The results deriving from the use of the software are encouraging because they allow to choose the best operational strategy in order to minimize the quantity of sludge produced by the plant
Development of a predicitive type-2 neurofuzzy controller
No full textA controller that combines the main characteristics and advantages of three different control methodologies is proposed for the control of systems with nonlinearities and uncertainties. A neural network predictive control approach is implemented modifying the output of a controller with a fuzzy logic structure that uses type-2 fuzzy sets. Neural networks are also used to optimize the membership function parameters. The proposed controller is tested by simulation for the control of a bioreactor characterized by bifurcation and parameter uncertainty
Adaptive Type-2 Fuzzy Logic Control of Non-Linear Processes
Get PDFThe main objective of this study is to provide a valid and effective approach for the design and development of an adaptive type-2 fuzzy controller (AT2FLC), based on the analysis of the nonlinear process dynamics and the use of an ANFIS technique for the optimization of the controller. The performance of the obtained AT2FLC, characterized by a few number of rules, is higher than the performance of a traditional type-2 fuzzy controller with a larger rule base. The proposed controller is particurarly suitable for the control of processes characterized by uncertainty and time varying parameters
- …
