Recurrent Higher Order Neural Observers for Anaerobic Processes
نویسندگان
چکیده
Anaerobic digestion is a bioprocess developed in oxygen absence by different populations of bacteria; these micro-organisms degrade progressively complex organic molecules. One of the most important applications of this process is the wastewater treatment, and it is very efficient to treat substrates with high organic load; besides the treated water, this process produces biogas, which is mainly composed of methane and carbon dioxide and it is considered as an alternative energy. However, anaerobic digestion process is very sensitive to changes on operating conditions and parameters, such as hydraulic and organic overloads, pH, temperature, etc. Then, control strategies are required in order to guarantee an ABSTRACT
منابع مشابه
Robust Backstepping Control of Induction Motor Drives Using Artificial Neural Networks and Sliding Mode Flux Observers
In this paper, using the three-phase induction motor fifth order model in a stationary twoaxis reference frame with stator current and rotor flux as state variables, a conventional backsteppingcontroller is first designed for speed and rotor flux control of an induction motor drive. Then in orderto make the control system stable and robust against all electromechanical parameter uncertainties a...
متن کاملA New Neural Observer for an Anaerobic Bioreactor
In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate variables of methanogenesis: biomass, substrate and inorganic carbon in a completely stirred tank reactor (CSTR). The recurrent high order neural network (RHONN) structure is based on the hyperbolic tangent as activation function. The learning algorithm is based o...
متن کاملA Recurrent Neural Network Model for Solving Linear Semidefinite Programming
In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...
متن کاملDiscrete-Time Reduced Order Neural Observers for Uncertain Nonlinear Systems
This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configu...
متن کاملEfficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks
Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...
متن کامل