Cylinder pressure based engine control systems use variables deduced from cylinder pressure as a feedback input. Monitoring of cylinder pressure is possible through various intrusive and nonintrusive sensors but cost of these sensors limits their use in the engines of on-road vehicles. In the present work, a recurrent neural network (RNN) is proposed which can reconstruct cylinder pressure of spark ignition engine. The network uses instantaneous crankshaft speed and motored pressure as inputs. Initially, parameters of two-zone model are tuned at limited number of experimental points, so that cylinder pressure predicted by model matches to that of experimental results. Further, the tuned model is used to generate large number of training data. Validation has been carried out using experimental as well as simulated pressure trace. It has been found that RNN can reconstruct cylinder pressure with reasonably good accuracy.
In this work, frequency response functions (FRFs) are used for estimation of cycle-by-cycle cylinder pressure and indicated torque waveform, using crankshaft speed fluctuations. The FRFs are mapped as a function of the discrete Fourier transform of engine speed, mean speed and manifold pressure using a multilayer neural network. The accuracy of the model is analysed using some of the parameters derived from the cylinder pressure. These include the indicated mean effective pressure and peak pressure. The load torque on the engine is also estimated using a closed-loop observer. The model is tested on a test rig consisting of single-cylinder engine coupled with an eddy current dynamometer. The results show that the model is suitable for the estimation of cylinder pressure and other variables related to it at the operating points where the cyclic variations are within a driveability limit.
Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the realtime controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air-fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly.
The present paper deals with the modeling of the removal of total arsenic As(T), trivalent arsenic As(III), and pentavalent arsenic As(V) from synthetic solutions containing total arsenic (0.167-2.0 mg/L), Fe (0.9-2.7 mg/L), and Mn (0.2-0.6 mg/L) in a batch reactor using Fe impregnated granular activated charcoal (GAC-Fe). Mass ratio of As(III) and As(V) in the solution was 1:1. Multi-layer neural network (MLNN) has been used and full factorial design technique has been applied for the selection of input data set. The developed models are able to predict the adsorption of arsenic species with an error limit of À0.3 to þ1.7%. Combination of MLNN with design of experiment has been able to generalize the MLNN with less number of experimental points.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.