Abstract
Process identification for composting of tobacco solid waste in an aerobic, adiabatic batch reactor was carried out using neural network-based models which utilized the nonlinear finite impulse response and nonlinear autoregressive model with exogenous inputs identification methods. Two soft sensors were developed for the estimation of conversion. The neural networks were trained by the adaptive gradient method using cascade learning. The developed models showed that the neural networks could be applied as intelligent software sensors giving a possibility of continuous process monitoring. The models have a potential to be used for inferential control of composting process in batch reactors.
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© 2007 Institute of Chemistry, Slovak Academy of Sciences
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Artikel in diesem Heft
- Spectrophotometric determination of microamounts of quercetin based on its complexation with copper(II)
- Oxygen evolution on Ti/Co3O4-coated electrodes in alkaline solution
- The zeta potential of kaolin suspensions measured by electrophoresis and electroacoustics
- Fluidization behavior of oil-contaminated sand
- Software sensors for monitoring of a solid waste composting process
- Calcined Ni—Al layered double hydroxide as a catalyst for total oxidation of volatile organic compounds: Effect of precursor crystallinity
- Thermodynamic possibilities and constraints of pure hydrogen production by a chromium, nickel, and manganese-based chemical looping process at lower temperatures
- Death kinetics of Escherichia coli in goat milk and Bacillus licheniformis in cloudberry jam treated by ohmic heating
- Topochemical models for anti-HIV activity of 1-alkoxy-5-alkyl-6-(arylthio)uracils
- Acidity, lipophilicity, solubility, absorption, and polar surface area of some ACE inhibitors
- Silver as anode in cryolite—alumina-based melts
- Multicomponent facile synthesis of novel dihydroazolopyrimidinyl carbamides
- Z. Platková, M. Polakovič, V. Štefuca, M. Vandáková, and M. Antošová: Selection of Carrier for Immobilization of Fructosyltransferase from Aureobasidium pullulans