Objective:The Chinese ADS proton Linac injector I composed of an electron cyclotron resonance ion source,a lowenergy beam transport line,a radio frequency quadrupole(RFQ)accelerator,a medium-energy beam transport(MEBT...Objective:The Chinese ADS proton Linac injector I composed of an electron cyclotron resonance ion source,a lowenergy beam transport line,a radio frequency quadrupole(RFQ)accelerator,a medium-energy beam transport(MEBT)line,a superconducting section and beam dump with design goal to achieve CW proton beam with beam current 10 mA and energy gain around 10 MeV.In order to maintain the stability of the RF system,to compensate for the beam load effect and to provide the GUI required for the operation of the RF power system,we have established a LLRF control system.Methods:This LLRF system for ADS proton Linac injector I in IHEP consists of RF reference system and LLRF controllers for oneRFQcavity,twoMEBTbunching cavities and 14 superconductive spoke cavities.We have adopted a new development mTCA.4-based hardware platform,and it has some characteristics such as faster bandwidth,standardized design,easy operation and maintenance and good expansibility.Results:The LLRF system has been improved and optimized and has now been put into operation.It takes into account the pulse and CW beam flow mode,feedforward and feedback control functions.Conclusion:With the help of this system,we achieved stable operationwith narrowpulses,long pulses,andCWmodes under different beam loads.This article describes the implementation of this system and presents some initial the CW beam acceleration results with the help of this system at the end of 2016.展开更多
The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water(DHW)production in residential buildings.This is done by deriving data-driven optimal heating schedules(used inter...The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water(DHW)production in residential buildings.This is done by deriving data-driven optimal heating schedules(used interchangeably with policies)automatically.The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism-an air-source heat pump(ASHP)in this case-at the highest possible efficiency.The proposed algorithm,while tested on an ASHP,is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well.Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings(NZEB)in the Netherlands are presented.These show a reduction of energy consumption for DHW by 20%using a computationally inexpensive heuristic approach,and 27%when using a more intensive hybrid ant colony optimization based method.The energy savings are strongly dependent on occupant comfort level.This is demonstrated in real-world settings for a low-consumption house where active control was performed using heuristics for 3.5months and resulted in energy savings of 27%(61 kW h).It is straightforward to extend the same models to perform automatic demand side management(ADSM)by treating the DHW vessel as a flexibility bearing device.展开更多
文摘Objective:The Chinese ADS proton Linac injector I composed of an electron cyclotron resonance ion source,a lowenergy beam transport line,a radio frequency quadrupole(RFQ)accelerator,a medium-energy beam transport(MEBT)line,a superconducting section and beam dump with design goal to achieve CW proton beam with beam current 10 mA and energy gain around 10 MeV.In order to maintain the stability of the RF system,to compensate for the beam load effect and to provide the GUI required for the operation of the RF power system,we have established a LLRF control system.Methods:This LLRF system for ADS proton Linac injector I in IHEP consists of RF reference system and LLRF controllers for oneRFQcavity,twoMEBTbunching cavities and 14 superconductive spoke cavities.We have adopted a new development mTCA.4-based hardware platform,and it has some characteristics such as faster bandwidth,standardized design,easy operation and maintenance and good expansibility.Results:The LLRF system has been improved and optimized and has now been put into operation.It takes into account the pulse and CW beam flow mode,feedforward and feedback control functions.Conclusion:With the help of this system,we achieved stable operationwith narrowpulses,long pulses,andCWmodes under different beam loads.This article describes the implementation of this system and presents some initial the CW beam acceleration results with the help of this system at the end of 2016.
文摘The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water(DHW)production in residential buildings.This is done by deriving data-driven optimal heating schedules(used interchangeably with policies)automatically.The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism-an air-source heat pump(ASHP)in this case-at the highest possible efficiency.The proposed algorithm,while tested on an ASHP,is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well.Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings(NZEB)in the Netherlands are presented.These show a reduction of energy consumption for DHW by 20%using a computationally inexpensive heuristic approach,and 27%when using a more intensive hybrid ant colony optimization based method.The energy savings are strongly dependent on occupant comfort level.This is demonstrated in real-world settings for a low-consumption house where active control was performed using heuristics for 3.5months and resulted in energy savings of 27%(61 kW h).It is straightforward to extend the same models to perform automatic demand side management(ADSM)by treating the DHW vessel as a flexibility bearing device.
