Processor-based organic Rankine cycle system for predictively-modeled recovery and conversion of thermal energy

1 . A system for collection and conversion of thermal energy to mechanical and/or electric energy, the system comprising:

a thermal energy recovery system for waste heat recovery, comprising an Organic Rankine Cycle (ORC) comprising a propellant heat exchanger, an expander and a condenser, and one or more heat exchangers comprising a heat medium for transferring waste heat from a combustion device to ORC propellant in the propellant heat exchanger for ORC operation;

sensors, operatively coupled to the thermal energy recovery system, the sensors being configured to produce system sensor data comprising operational data associated with environmental conditions and ORC-based operational parameters;

a control circuit comprising a predictive module, wherein the predictive module comprises operational parameters trained on a neural network using node-based pattern classification and regression tailored for ORC net power optimization by capturing non-linear relationships and temporal sequences characteristic of the ORC system's operation and configured to operate the thermal energy recovery system of the system under configured target operational values, based on operational conditions comprising waste heat recovery and secondary power generation from the ORC, for maximizing net power output from the ORC,

wherein the control circuit is configured to receive and process the produced sensor data to evaluate whether the thermal energy recovery system is projected to deviate from the target operational values within a configured time period,

and wherein the control circuit is configured to transmit control signals to dynamically modify operation of the thermal energy recovery system such that further produced system sensor data meets the target operational values, wherein the control signals are configured to adjust thermal or operational conditions of the system by modifying heat input and heat extraction of at least two thermal sources comprising thermal fluids, jacket water, exhaust and/or ORC propellant to meet the target operational values.

2 . The system of claim 1 , wherein the operational parameters comprise one or more of ORC operational parameters, thermo-electric generator (TEG) operational parameters, engine operational parameters, gas compressor operational parameters and general operational parameters,

wherein the ORC operational parameters comprise one or more of:

power generated by the expander, expander speed, ORC system parasitic loads, ORC propellant temperatures throughout an ORC process, ORC propellant pressures throughout the ORC process, ORC mass flow rate of propellant working fluid, and thermal fluid temperature and/or flow rates entering and exiting the ORC,

wherein the TEG operational parameters comprise one or more of:

TEG power generation, engine jacket water entering and exiting the TEG, engine exhaust temperature entering and exiting the TEG, and thermal fluid temperature entering and exiting the TEG,

wherein the engine operational parameters comprise one or more of:

engine speed, engine load, engine torque, engine parasitic loads, radiator fan speed, radiator fan load, engine jacket water temperatures, engine jacket water flow rate, engine jacket water discharge from engine temperature, engine jacket water return to engine temperature, engine jacket water discharge from radiator temperature, engine jacket water discharge to radiator temperature, engine exhaust flow rate, and engine exhaust temperature,

wherein the gas compressor operational parameters comprise one or more of:

gas compressor flow rate, gas compressor temperatures in and/or out of each stage of compression, gas compressor jack shaft speed, gas compressor jack shaft torque,

and wherein the general operational parameters throughout the system comprise one or more of:

thermal fluid temperatures, pressures and/or flow rates, engine jacket water temperatures pressures and/or flow rates, engine exhaust temperatures, pressures and/or mass flow rates, propellant temperatures, control valves position information, electric drives motor speed, electric drives motor torque, pump flow rates and/or pressures, generator output, and ambient air temperature.

3 . The system of claim 1 , wherein at least one of an engine radiator and/or the condenser of the ORC is configured to provide cooling capability to a cooling side of a thermo-electric generator (TEG).

4 . The system of claim 1 , wherein the operational parameters are configured as a predictive model comprising one or more instances based on one or more vectors of the neural network representing features of an input and a corresponding output relating to one or more target operational values.

5 . The system of claim 4 , wherein the predictive model comprises classifier data associated with pattern recognition performed on previous sensor data.

6 . The system of claim 5 , wherein the control circuit is configured to map the produced sensor data with the classifier data.

7 . The system of claim 1 , wherein the control signals are configured to control thermal energy from a thermal energy source and ORC in the system to meet the target operational values without disrupting the operation of the system.

8 . The system of claim 1 , wherein the control signals are configured to control flow paths in the system to either input heat into, or take heat out of the exhaust, thermal fluids and/or the jacket water and/or ORC propellant to meet the target operational values without disrupting the operation of the system.

9 . A method for collection and conversion of thermal energy to mechanical energy in a system, comprising:

receiving system sensor data from a thermal energy recovery system for waste heat recovery comprising operational data associated with environmental conditions and ORC-based operational parameters, the thermal energy recovery system comprising an Organic Rankine Cycle (ORC) comprising a propellant heat exchanger, an expander and a condenser, and one or more heat exchangers comprising a heat medium for transferring waste heat from a combustion device to ORC propellant in the propellant heat exchanger for ORC operation;

receiving, via a control circuit comprising a predictive module, operational parameters trained on a neural network using node-based pattern classification and regression configured to operate the thermal energy recovery system toward configured target operational values based on operational conditions comprising waste heat recovery and secondary power generation from the ORC, for maximizing net power output in the system,

processing, via the control circuit, the sensor data to evaluate whether the thermal energy recovery system is projected to deviate from the target operational values within a configured time period; and

transmitting, via the control circuit, control signals to dynamically modify operation of the thermal energy recovery system of the system such that further produced system sensor data meets the target operational values, wherein the control signals are configured to adjust thermal or operational conditions by modifying heat input and heat extraction out of at least two thermal sources comprising thermal fluids, jacket water, exhaust and/or ORC propellant to meet the target operational values.

10 . The method of claim 9 , wherein the operational parameters comprise one or more of ORC operational parameters, thermo-electric generator (TEG) operational parameters, engine operational parameters, compressor operational parameters and general operational parameters,

wherein the ORC operational parameters comprise one or more of:

power generated by the expander, expander speed, ORC system parasitic loads, ORC propellant temperatures throughout an ORC process, ORC propellant pressures throughout the ORC process, ORC mass flow rate of propellant working fluid, and thermal fluid temperature and/or flow rates entering and exiting the ORC,

wherein the TEG operational parameters comprise one or more of:

TEG power generation, engine jacket water entering and exiting the TEG, engine exhaust temperature entering and exiting the TEG, and thermal fluid temperature entering and exiting the TEG,

wherein the engine operational parameters comprise one or more of:

engine speed, engine load, engine torque, engine parasitic loads, radiator fan speed, radiator fan load, engine jacket water temperatures, engine jacket water flow rate, engine jacket water discharge from engine temperature, engine jacket water return to engine temperature, engine jacket water discharge from radiator temperature, engine jacket water discharge to radiator temperature, engine exhaust flow rate, and engine exhaust temperature,

wherein the compressor operational parameters comprise one or more of:

compressor flow rate, compressor temperatures in and/or out of each stage of compression, compressor jack shaft speed, compressor jack shaft torque,

and wherein the general operational parameters throughout the system comprise one or more of:

thermal fluid temperatures pressures and/or flow rates, engine jacket water temperatures pressures and/or flow rates, engine exhaust temperatures pressures and/or mass flow rates, propellant temperatures pressures and/or flow rates, control valve position information, electric drive motor speed, electric drive motor torque, pump flow rates and/or pressures, generator output, and ambient air temperature.

11 . The method of claim 9 , further comprising providing cooling to the system via at least one of the radiator and/or the condenser of the ORC to provide a steady temperature differential to a thermo-electric generator (TEG).

12 . The method of claim 9 , wherein the operational parameters are configured as a predictive model comprising one or more instances based on one or more vectors of the neural network representing features of an input and a corresponding output relating to one or more target operational values.

13 . The method of claim 12 , wherein the predictive model comprises classifier data associated with pattern recognition performed on previous sensor data.

14 . The method of claim 13 , wherein the control circuit is configured to map the produced sensor data with the classifier data.

15 . The method of claim 9 , wherein the control signals are configured to control thermal energy from a thermal energy source and ORC in the system to meet the target operational values without disrupting the operation of the system.

16 . The method of claim 9 , wherein the control signals are configured to control flow paths in the system to either input heat into, or take heat out of the exhaust and/or thermal fluids to meet the target operational values without disrupting the operation of the system.

17 . A system for collection and conversion of thermal energy to mechanical and/or electric energy, the system comprising:

a thermal energy recovery system for waste heat recovery, comprising an Organic Rankine Cycle (ORC) comprising a propellant heat exchanger, an expander, a condenser, and one or more heat exchangers comprising a heat medium for transferring waste heat from a combustion device to ORC propellant in the propellant heat exchanger for ORC operation;

sensors, operatively coupled to the thermal energy recovery system and the waste heat recovery system, the sensors being configured to produce system sensor comprising operational data associated with environmental conditions and ORC-based operational parameters;

a control circuit comprising a predictive module, wherein the predictive module comprises operational parameters for a predictive model trained on a neural network configured to model nonlinear and time-dependent ORC system behavior, the modeling comprising generating vector-based representations of sensor-derived input features and associating such representations with predicted operational outputs corresponding to one or more target operational values, the predictions reflecting waste-heat recovery conditions and secondary power generation from the ORC, for maximizing net power output in the system,

wherein the control circuit is configured to receive and process the produced sensor data to evaluate whether the sensor data is projected to deviate from the target operational values within a configured time period,

and wherein the control circuit is configured to transmit control signals to dynamically modify operation of the thermal energy recovery system such that further produced system sensor data meets the target operational values, wherein the control signals are configured to adjust thermal or operational conditions by modifying heat input and heat extraction of at least two thermal sources comprising thermal fluids, jacket water, exhaust and/or ORC propellant to meet the target operational values.

18 . The system of claim 17 , wherein the operational parameters comprise one or more of:

power generated by the expander, expander speed, ORC system parasitic loads, ORC propellant temperatures throughout an ORC process, ORC propellant pressures throughout the ORC process, ORC mass flow rate of propellant working fluid, ORC flow rate of propellant working fluid, thermal energy transferred into and/or out of the system,

control valve position information, an electric drive motor speed, an electric drive motor torque, pump flow rates and/or pressures, generator output, and ambient air temperature.

19 . The system of claim 17 , wherein the condenser of the ORC is configured to provide cooling to the system.

20 . The system of claim 17 , wherein the predictive model comprises classifier data associated with pattern recognition performed on previous sensor data, and wherein the control circuit is configured to map the produced sensor data with the classifier data.