Presses for vulcanizing industrial rubber products

1987 ◽  
Vol 23 (6) ◽  
pp. 270-273
Author(s):  
M. R. Sakolishch ◽  
V. A. Rusakov ◽  
A. V. Popov
Keyword(s):  
Industrial Rubber

Reprocessing of industrial rubber waste in a mixture with shale (Short Communication)

2008 ◽  
Vol 42 (2) ◽  
pp. 93-94 ◽  
Author(s):  
E. E. Gorlova ◽  
B. K. Nefedov ◽  
E. G. Gorlov ◽  
A. A. Ol’gin
Keyword(s):  
Short Communication ◽  
Rubber Waste ◽  
Industrial Rubber

Rapid Solar-thermal Decarbonization of Methane in a Fluid-wall Aerosol Flow Reactor -- Fundamentals and Application

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Jeffrey Wyss ◽  
Janna Martinek ◽  
Michael Kerins ◽  
Jaimee K Dahl ◽  
Alan Weimer ◽  
...  
Keyword(s):  
Flow Reactor ◽  
Rapid Heating ◽  
Solar Thermal ◽  
Reactor Wall ◽  
Thermal Mass ◽  
Chemical Processing ◽  
Aerosol Flow ◽  
The Past ◽  
Industrial Rubber ◽  
Products Of Reaction

A graphite fluid-wall aerosol flow reactor heated with concentrated sunlight has been developed over the past five years for the solar-thermal decarbonization of methane. The fluid-wall is provided by an inert or compatible gas that prevents contact of reactants and products of reaction with a graphite reaction tube. The reactor provides for a low thermal mass that is compatible with intermittent sunlight and the graphite construction allows rapid heating/cooling rates and ultra-high temperatures. The decarbonization of methane has been demonstrated at over 90% for residence times on the order of 10 milliseconds at a reactor wall temperature near 2000 K. The carbon black resulting from the dissociation of methane is nanosized, amorphous, and ash-free and can be used for industrial rubber production. The hydrogen can be supplied to a pipeline and used for chemical processing or to supply fuel cell vehicles.

scholarly journals Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Huaiping Jin ◽  
Jiangang Li ◽  
Meng Wang ◽  
Bin Qian ◽  
Biao Yang ◽  
...  
Keyword(s):  
Gaussian Process Regression ◽  
Soft Sensor ◽  
Viscosity Measurement ◽  
Finite Mixture ◽  
Just In Time ◽  
Mixing Process ◽  
Industrial Rubber ◽  
Viscosity Prediction ◽  
Mooney Viscosity ◽  
Input Variables

The lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation- (MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. This method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. The proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.

Nature-Saving Industrial Utilization Technology of Industrial Rubber Waste by the Pyrolysis Method

2015 ◽  
Vol 19 (8) ◽  
pp. 15 ◽  
Author(s):  
V.I. Nazarov ◽  
D.A. Makarenkov ◽  
E.A. Barinskii ◽  
S.N. Kramorova
Keyword(s):  
Rubber Waste ◽  
Pyrolysis Method ◽  
Industrial Utilization ◽  
Industrial Rubber

Mechanizing the diffusive modification of industrial rubber articles

1987 ◽  
Vol 23 (3) ◽  
pp. 116-118
Author(s):  
E. I. Gerashchenko ◽  
E. V. Prokazova
Keyword(s):  
Industrial Rubber
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