Tank level control is ubiquitous in industry. The focus of this paper is on accurate liquid level control in single tank systems which can be actuated continuously and modulation of the level setpoint is also required...Tank level control is ubiquitous in industry. The focus of this paper is on accurate liquid level control in single tank systems which can be actuated continuously and modulation of the level setpoint is also required, for example in cascade control loops or supervisory Model Predictive Control (MPC) applications. To avoid common problems encountered when using fixed gain or adaptive/gain scheduled schemes, an accurate technique based around feedback linearization and Proportional Integral (PI) control is introduced. This simple controller can maintain linear performance over the full operating range of a uniform tank. As will be demonstrated, the implementation overhead compared to a regular PI controller is negligible, making it ideal for industrial implementation. Implementation details and parameter identification for adaptive implementation are discussed. Simulations coupled with experimental results using a large-scale laboratory level control system using commercial industrial control equipment validate the approach, and illustrate its effectiveness for both level tracking and disturbance rejection.展开更多
Raman spectroscopy is a noninvasive,nondestructive analytical method capable of determining the biochemical constituents based on molecular vibrations.It does not require sample preparation or pretreatment.However,the...Raman spectroscopy is a noninvasive,nondestructive analytical method capable of determining the biochemical constituents based on molecular vibrations.It does not require sample preparation or pretreatment.However,the use of Raman spectroscopy for in vivo clinical applications will depend on the feasibility of measuring Raman spectra in a relatively short time period(a few seconds).In this work,a fast dispersive-type nearinfrared(NIR)Raman spectroscopy system and a skin Raman probe were developed to facilitate real-time,noninvasive,in vivo human skin measurements.Spectrograph image aberration was corrected by a parabolic-line fiber array,permitting complete CCD vertical binning,thereby yielding a 16-fold improvement in signal-to-noise ratio.Good quality in vivo skin NIR Raman spectra free of interference from fiber fluorescence and silica Raman scattering can be acquired within one second,which greatly facilitates practical noninvasive tissue characterization and clinical diagnosis.Currently,we are conducting a large clinical study of various skin diseases in order to develop Raman spectroscopy into a useful tool for non-invasive skin cancer detection.Intermediate data analysis results are presented.Recently,we have also successfully developed a technically more challenging endoscopic Laser-Raman probe for early lung cancer detection.Preliminary in vivo results from endoscopic lung Raman measurements are discussed.展开更多
The world is experiencing a fourth industrial revolution.Rapid development of technologies is advancing smart infrastructure opportunities.Experts observe decarbonisation,digitalisation and decentralisation as the mai...The world is experiencing a fourth industrial revolution.Rapid development of technologies is advancing smart infrastructure opportunities.Experts observe decarbonisation,digitalisation and decentralisation as the main drivers for change.In electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for op-erators of low-inertia energy systems.In the absence of reliable real-time demand forecasting measures,effective decentralised demand-side energy planning is often problematic.In this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is consumed.The methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy consumption.Thus,contributing to a reduction in the demand of state-owned generation power plants.The forecasting session is constructed initially through analysis of a chronological sequence of discrete observations.Historical demand data shows behaviour that allows the use of dimensionality reduction techniques.Combined with piecewise interpolation an electricity demand forecasting methodology is formulated.Solutions of short-term forecasting problems provide credible predictions for energy demand.Calculations for medium-term forecasts that extend beyond 6-months are also very promising.The forecasting method provides a way to advance a novel decentralised informatics,optimisa-tion and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service.展开更多
文摘Tank level control is ubiquitous in industry. The focus of this paper is on accurate liquid level control in single tank systems which can be actuated continuously and modulation of the level setpoint is also required, for example in cascade control loops or supervisory Model Predictive Control (MPC) applications. To avoid common problems encountered when using fixed gain or adaptive/gain scheduled schemes, an accurate technique based around feedback linearization and Proportional Integral (PI) control is introduced. This simple controller can maintain linear performance over the full operating range of a uniform tank. As will be demonstrated, the implementation overhead compared to a regular PI controller is negligible, making it ideal for industrial implementation. Implementation details and parameter identification for adaptive implementation are discussed. Simulations coupled with experimental results using a large-scale laboratory level control system using commercial industrial control equipment validate the approach, and illustrate its effectiveness for both level tracking and disturbance rejection.
基金supported by the National Cancer Institute of Canada with funds from the Canadian Cancer Society,the Canadian Institutes of Health Research(Grant No.PPP-79109 and MOP-85011)the Canadian Dermatology Foundation,the VGH&UBC Hospital Foundation In It for Life Fund,and the BC Hydro Employees Community Services Fund.
文摘Raman spectroscopy is a noninvasive,nondestructive analytical method capable of determining the biochemical constituents based on molecular vibrations.It does not require sample preparation or pretreatment.However,the use of Raman spectroscopy for in vivo clinical applications will depend on the feasibility of measuring Raman spectra in a relatively short time period(a few seconds).In this work,a fast dispersive-type nearinfrared(NIR)Raman spectroscopy system and a skin Raman probe were developed to facilitate real-time,noninvasive,in vivo human skin measurements.Spectrograph image aberration was corrected by a parabolic-line fiber array,permitting complete CCD vertical binning,thereby yielding a 16-fold improvement in signal-to-noise ratio.Good quality in vivo skin NIR Raman spectra free of interference from fiber fluorescence and silica Raman scattering can be acquired within one second,which greatly facilitates practical noninvasive tissue characterization and clinical diagnosis.Currently,we are conducting a large clinical study of various skin diseases in order to develop Raman spectroscopy into a useful tool for non-invasive skin cancer detection.Intermediate data analysis results are presented.Recently,we have also successfully developed a technically more challenging endoscopic Laser-Raman probe for early lung cancer detection.Preliminary in vivo results from endoscopic lung Raman measurements are discussed.
基金The first author wishes to acknowledge the financial support pro-vided by Teesside University and the Doctoral Training Alliance(DTA)scheme in Energy.The authors also acknowledge elements of the work was carried out as part of the REACT project(01/01/2019-31/12/2022)which is co-funded by the EU’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No.824395.
文摘The world is experiencing a fourth industrial revolution.Rapid development of technologies is advancing smart infrastructure opportunities.Experts observe decarbonisation,digitalisation and decentralisation as the main drivers for change.In electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for op-erators of low-inertia energy systems.In the absence of reliable real-time demand forecasting measures,effective decentralised demand-side energy planning is often problematic.In this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is consumed.The methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy consumption.Thus,contributing to a reduction in the demand of state-owned generation power plants.The forecasting session is constructed initially through analysis of a chronological sequence of discrete observations.Historical demand data shows behaviour that allows the use of dimensionality reduction techniques.Combined with piecewise interpolation an electricity demand forecasting methodology is formulated.Solutions of short-term forecasting problems provide credible predictions for energy demand.Calculations for medium-term forecasts that extend beyond 6-months are also very promising.The forecasting method provides a way to advance a novel decentralised informatics,optimisa-tion and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service.
