A (Somewhat) New Solution to the Binding Problem

A gas turbine cycle in which air is compressed to superatmospheric pressure, water is circulated in heat exchange relation with the compressed air to convert the sensible heat therein to heated water, the compressed air is humidified using the heated water, the humidified air is heated with sensible heat in the turbine exhaust gases, and a solid fuel such as coal is converted to a motive fluid for driving the gas turbine by substoichiometrically burning solid fuel under superatmospheric pressure using oxygen from the heated humidified air for combustion of the solid fuel and to produce combustible fuel gas and char, above stoichiometrically and adiabatically burning the char under superatmospheric pressure by supplying the heated humidified air in sufficient quantity to provide oxygen for combustion of the char and to control the temperature of such combustion, thereby to produce char combustion gases containing oxygen, cleaning the combustible fuel gas and the char combustion gasses to remove particulate materials therefrom, and burning the combustible fuel gas under superatmospheric pressure, oxygen for combustion of the gases being supplied by the char combustion gases and the heated humidified air, and the temperature of such gas combustion being controlled by the quantity of heated humidified air supplied for burning the fuel gas and the char combustion gases, the products of such gas combustion providing the motive fluid for driving the turbine.

[1]  Lokendra Shastri,et al.  Multiple instantiation and rule mediation in SHRUTI , 2004, Connect. Sci..

[2]  P. V. Loocke,et al.  QNET: a quantum mechanical neural network. A new connectionist architecture and its relevance for variable binding and constraint satisfaction problems , 1995 .

[3]  Antony Browne,et al.  Connectionist inference models , 2001, Neural Networks.

[4]  R. Sun.  On Variable Binding in Connectionist Networks , 1992 .

[5]  Dana H. Ballard,et al.  Parallel Logical Inference and Energy Minimization , 1986, AAAI.

[6]  Jerome A. Feldman,et al.  Dynamic connections in neural networks , 1990, Biological Cybernetics.

[7]  Antony Browne,et al.  Connectionist variable binding , 1999, Expert Syst. J. Knowl. Eng..

[8]  Hilbert J. Kappen,et al.  Dynamic linking in stochastic networks , 1995 .

[9]  R. O’Reilly,et al.  Three forms of binding and their neural substrates: Alternatives to temporal synchrony , 2003 .

[10]  Lokendra Shastri,et al.  A computational model of episodic memory formation in the hippocampal system , 2001, Neurocomputing.

[11]  Lokendra Shastri,et al.  Advances in SHRUTI—A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony , 1999, Applied Intelligence.

[12]  Lokendra Shastri,et al.  Probabilistic Inference and Learning in a Connectionist Causal Network , 2000 .

[13]  Joachim Diederich,et al.  Connectionist Recruitment Learning , 1988, ECAI.

[14]  Z. Harris,et al.  Foundations of Language , 1940 .

[15]  John K. Tsotsos.  Analyzing vision at the complexity level , 1990, Behavioral and Brain Sciences.

[16]  C. Chabris,et al.  Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events , 1999, Perception.