Optimization of the Fiber Cement Composite Process

Negro, Carlos; Alonso, Andoni; Blanco, A.; Tijero, J.

doi:10.1021/ie048907j

Cited by 13 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FBRM, also known as scanning laser microscopy, is detailed elsewhere and is briefly summarized here. The methodology of this technique is based on a highly focused laser beam scanning across particles in a suspension at a fixed speed.…”

Section: Methodsmentioning

confidence: 99%

Synthesis and evaluation of P(AM‐b‐DADMAC) as fixative for dissolved and colloidal substances

Wang¹,

Zhang

et al. 2013

J of Applied Polymer Sci

View full text Add to dashboard Cite

Acrylamide (AM) and diallyldimethyl ammonium chloride (DADMAC) block copolymer P(AM-b-DADMAC) was synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization method. The successful polymerization of controlled composition and polydispersity index of the block copolymer was confirmed by FTIR, 1 H-NMR, and size exclusion chromatography analyses. Differential scanning calorimetry and thermal gravimetric analysis further confirmed the interaction between the trithioester groups of RAFT agent and alkenyl groups of AM and DADMAC in the final product. With turbidity, chemical oxygen demand and focused beam reflectance measurement measurements, the block copolymer was proved to be effective at fixing dissolved and colloidal substances onto papermaking fibers, thus removing these substances from the water phase where they have a tendency to aggregate and form troublesome deposits.

show abstract

Section: Methodsmentioning

confidence: 99%

Synthesis and evaluation of P(AM‐b‐DADMAC) as fixative for dissolved and colloidal substances

Wang¹,

Zhang

et al. 2013

J of Applied Polymer Sci

View full text Add to dashboard Cite

show abstract

“…In brief, ANN has basic elements, which are three layers (so‐called input, hidden, and output layers), weights, bias, and transfer functions 37–39. It should be noted that there can be more than one hidden layer, but usually a network containing one hidden layer and numerous neurons is enough to perform a task.…”

Section: Methodsmentioning

confidence: 99%

“…In brief, ANN has basic elements, which are three layers (so-called input, hidden, and output layers), weights, bias, and transfer functions. [37][38][39] It should be noted that there can be more than one hidden layer, but usually a network containing one hidden layer and numerous neurons is enough to perform a task. Each neuron, or node, in the input layer corresponding to each independent variable sends a weighted vector of the variable to all neurons in the hidden layer.…”

Section: Modeling the Relationship Between The Styrene Conversion Andmentioning

confidence: 99%

Prediction of styrene conversion of polystyrene/natural rubber graft copolymerization using reaction conditions: Central composite design versus artificial neural networks

Aroonsingkarat

Hansupalak

2012

J of Applied Polymer Sci

View full text Add to dashboard Cite

Gross copolymer or the total product of graft copolymerization of polystyrene (PS) and rubber, prepared via emulsion polymerization using a redox initiator, is used to investigate the utilization of central composite design and artificial neural network (ANN) approaches in correlating the graft copolymerization conditions to the monomer conversion. The conditions were manipulated by changing four factors: reaction temperature and time, percentage of deproteinized natural rubber (DPNR) in the rubber mixture also containing NR, and amount of chain transfer agent. For DPNR preparation, the incorporation of ultrasound energy into a deproteinizing method (i.e., urea treatment) was preexamined. A shorter reaction time, a lower total nitrogen content, and no agglomeration of rubber particles suggest the success of the incorporation. Results exhibit that the relationship between those factors and the response can be better described by the ANN model, which is further proved to be an excellent tool for the prediction of the conversion at other reaction conditions. In addition, the thermal behavior of gross copolymer is similar to its parents, the rubber and neat PS, but more to the former owing to the larger amount of rubber component. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

show abstract

“…Mainly, partial least squares (PLS) method1–16 is used as a modeling method for the soft sensors. Also, principle component regression (PCR) method,14, 17, 18 nonlinear PLS method,7, 10, 19–23 artificial neural network,10, 13, 18, 24–51 support vector machine based regression method,52–58 and so on, are researched as the soft sensor method. By using soft sensors, a value of objective variables can be estimated with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Development of a new soft sensor method using independent component analysis and partial least squares

2008

View full text Add to dashboard Cite

in Wiley InterScience (www.interscience.wiley.com).Soft sensors are used widely to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. To cope with this problem, a regression model can be updated. However, if the model is updated with an abnormal sample, the predictive ability can deteriorate. We have applied the independent component analysis (ICA) method to the soft sensor to increase fault detection ability. Then, we have tried to increase the predictive accuracy. By using the ICA-based fault detection and classification model, the objective variable can be predicted, updating the PLS model appropriately. We analyzed real industrial data as the application of the proposed method. The proposed method achieved higher predictive accuracy than the traditional one. Furthermore, the nonsteady state could be detected as abnormal correctly by the ICA model.

show abstract

Optimization of the Fiber Cement Composite Process

Cited by 13 publications

References 19 publications

Synthesis and evaluation of P(AM‐b‐DADMAC) as fixative for dissolved and colloidal substances

Synthesis and evaluation of P(AM‐b‐DADMAC) as fixative for dissolved and colloidal substances

Prediction of styrene conversion of polystyrene/natural rubber graft copolymerization using reaction conditions: Central composite design versus artificial neural networks

Development of a new soft sensor method using independent component analysis and partial least squares

Contact Info

Product

Resources

About