Abstract: A method for controlling a membrane distillation system includes determining whether there is a day time or a night time at a location of a solar collector system associated with the membrane distillation system; applying a first control mode during the day time to a flow velocity of a feed used by the membrane distillation system; and applying a second control mode, different from the first control scheme, during the night time, to the feed. The first control scheme is a model-free mode.

Abstract: A method for classifying input data includes receiving the input data that describe an object, wherein the input data corresponds to plural classes; associating the input data with voxels that describe the object; calculating a real-number sequence X(n), which is associated with a measured parameter P that describes the object; quantizing the real-number sequence X(n) to generate a finite set sequence Q(n), where n describes a number of levels; generating a voxel-based weight matrix for each class of the input data; and calculating a score S for each class of the plural classes, based on a corresponding voxel-based weight matrix. The score S is a number that indicates a likelihood that the input data associated with a given sample belongs to a class of the plural classes.

Abstract: Various examples of systems and methods are provided for Lyapunov control for uncertain systems. In one example, a system includes a process plant and a robust Lyapunov controller configured to control an input of the process plant. The robust Lyapunov controller includes an inner closed loop Lyapunov controller and an outer closed loop error stabilizer. In another example, a method includes monitoring a system output of a process plant; generating an estimated system control input based upon a defined output reference; generating a system control input using the estimated system control input and a compensation term; and adjusting the process plant based upon the system control input to force the system output to track the defined output reference. An inner closed loop Lyapunov controller can generate the estimated system control input and an outer closed loop error stabilizer can generate the system control input.

Abstract: A method for controlling a direct contact membrane distillation (DCMD) system, the method including modeling the DCMD system with differential-algebraic equations (DAEs), wherein the DAEs include process states {tilde over (x)}(tk) and an input state u(tk); selecting a value for the input variable u(tk) for a time tk; estimating the process states {tilde over (x)}(tk) based on the DAEs and the input state u(tk); checking that a boundedness function G applied to the process states {tilde over (x)}(tk) is smaller than a desired steady-state point ?e; and minimizing an objective function, which depends on the process states {tilde over (x)}(tk) and the input state u(tk), to determine an updated input state u(tk+1) for a next time tk+1. The process states {tilde over (x)}(tk) include temperatures and heat flow rates.

Abstract: Various examples of methods and systems are provided for soft sensing of system parameters in membrane distillation (MD). In one example, a system includes a MD module comprising a feed side and a permeate side separated by a membrane boundary layer; and processing circuitry configured to estimate feed solution temperatures and permeate solution temperatures of the MD module using monitored outlet temperatures of the feed side and the permeate side. In another example, a method includes monitoring outlet temperatures of a feed side and a permeate side of a MD module to determine a current feed outlet temperature and a current permeate outlet temperature; and determining a plurality of estimated temperature states of a membrane boundary layer separating the feed side and the permeate side of the MD module using the current feed outlet temperature and the current permeate outlet temperature.

Abstract: Various examples are provided that are related to boundary control in membrane distillation (MD) processes. In one example, a system includes a membrane distillation (MD) process comprising a feed side and a permeate side separated by a membrane boundary layer; and processing circuitry configured to control a water production rate of the MD process based at least in part upon a distributed heat transfer across the membrane boundary layer. In another example, a method includes determining a plurality of estimated temperature states of a membrane boundary layer separating a feed side and a permeate side of a membrane distillation (MD) process; and adjusting inlet flow rate or inlet temperature of at least one of the feed side or the permeate side to maintain a difference temperature along the membrane boundary layer about a defined reference temperature based at least in part upon the plurality of estimated temperature states.

Abstract: A method for classifying input data includes receiving the input data that describe an object, wherein the input data corresponds to plural classes; associating the input data with voxels that describe the object; calculating a real-number sequence X(n), which is associated with a measured parameter P that describes the object; quantizing the real-number sequence X(n) to generate a finite set sequence Q(n), where n describes a number of levels; generating a voxel-based weight matrix for each class of the input data; and calculating a score S for each class of the plural classes, based on a corresponding voxel-based weight matrix. The score S is a number that indicates a likelihood that the input data associated with a given sample belongs to a class of the plural classes.

Abstract: A method for generating a feature associated with input data includes receiving the input data; projecting the input data with a set of square functions ?nh2 of the Schrödinger operator; selecting the feature to be a number of the negative eigenvalues ?nh of the Schrödinger operator; and classifying the input data based on the feature.

Abstract: A method for controlling a membrane distillation system includes determining whether there is a day time or a night time at a location of a solar collector system associated with the membrane distillation system; applying a first control mode during the day time to a flow velocity of a feed used by the membrane distillation system; and applying a second control mode, different from the first control scheme, during the night time, to the feed. The first control scheme is a model-free mode.

Abstract: A method for assessing a state of a cardiovascular system includes receiving a blood pressure Pa and a blood flow Qa of the cardiovascular system; calculating with a fractional-order, viscoelastic Windkessel model an arterial compliance C? of the cardiovascular system; and evaluating the state of the cardiovascular system based on a fractional-order parameter ? associated with the fractional-order, viscoelastic Windkessel model.

Abstract: A method for suppressing a water line in an acquired magnetic resonance spectra (MRS) y, the method including receiving (800) the acquired MRS spectra y; estimating (802) a water peak ywp for the acquired MRS spectra y using squared eigenfunctions (?) of a Schrödinger operator H; and removing (804) the water peak ywp from the acquired MRS y to obtain a suppressed water MRS spectra yws. The suppressed water MRS spectra yws includes a signature of one or more metabolites.

Abstract: A method for suppressing a water line in an acquired magnetic resonance spectra (MRS) y, the method including receiving (800) the acquired MRS spectra y; estimating (802) a water peak ywp for the acquired MRS spectra y using squared eigenfunctions (?) of a Schrödinger operator H; and removing (804) the water peak ywp from the acquired MRS y to obtain a suppressed water MRS spectra yws. The suppressed water MRS spectra yws includes a signature of one or more metabolites.

Abstract: Various examples are related to smart membranes for monitoring membrane based process such as, e.g., membrane distillation processes. In one example, a membrane, includes a porous surface and a plurality of sensors (e.g., temperature, flow and/or impedance sensors) mounted on the porous surface. In another example, a membrane distillation (MD) process includes the membrane. Processing circuitry can be configured to monitor outputs of the plurality of sensors. The monitored outputs can be used to determine membrane degradation, membrane fouling, or to provide an indication of membrane replacement or cleaning. The sensors can also provide temperatures or temperature differentials across the porous surface, which can be used to improve modeling or control the MD process.

Abstract: Various examples are related to smart membranes for monitoring membrane based process such as, e.g., membrane distillation processes. In one example, a membrane, includes a porous surface and a plurality of sensors (e.g., temperature, flow and/or impedance sensors) mounted on the porous surface. In another example, a membrane distillation (MD) process includes the membrane. Processing circuitry can be configured to monitor outputs of the plurality of sensors. The monitored outputs can be used to determine membrane degradation, membrane fouling, or to provide an indication of membrane replacement or cleaning. The sensors can also provide temperatures or temperature differentials across the porous surface, which can be used to improve modeling or control the MD process.

Abstract: Various examples of methods and systems are provided for soft sensing of system parameters in membrane distillation (MD). In one example, a system includes a MD module comprising a feed side and a permeate side separated by a membrane boundary layer; and processing circuitry configured to estimate feed solution temperatures and permeate solution temperatures of the MD module using monitored outlet temperatures of the feed side and the permeate side. In another example, a method includes monitoring outlet temperatures of a feed side and a permeate side of a MD module to determine a current feed outlet temperature and a current permeate outlet temperature; and determining a plurality of estimated temperature states of a membrane boundary layer separating the feed side and the permeate side of the MD module using the current feed outlet temperature and the current permeate outlet temperature.

Abstract: Various examples of systems and methods are provided for Lyapunov control for uncertain systems. In one example, a system includes a process plant and a robust Lyapunov controller configured to control an input of the process plant. The robust Lyapunov controller includes an inner closed loop Lyapunov controller and an outer closed loop error stabilizer. In another example, a method includes monitoring a system output of a process plant; generating an estimated system control input based upon a defined output reference; generating a system control input using the estimated system control input and a compensation term; and adjusting the process plant based upon the system control input to force the system output to track the defined output reference. An inner closed loop Lyapunov controller can generate the estimated system control input and an outer closed loop error stabilizer can generate the system control input.

Abstract: Various examples are provided that are related to boundary control in membrane distillation (MD) processes. In one example, a system includes a membrane distillation (MD) process comprising a feed side and a permeate side separated by a membrane boundary layer; and processing circuitry configured to control a water production rate of the MD process based at least in part upon a distributed heat transfer across the membrane boundary layer. In another example, a method includes determining a plurality of estimated temperature states of a membrane boundary layer separating a feed side and a permeate side of a membrane distillation (MD) process; and adjusting inlet flow rate or inlet temperature of at least one of the feed side or the permeate side to maintain a difference temperature along the membrane boundary layer about a defined reference temperature based at least in part upon the plurality of estimated temperature states.

Abstract: A method and system can analyze, reconstruct, and/or denoise an image. The method and system can include interpreting a signal as a potential of a Schrödinger operator, decomposing the signal into squared eigenfunctions, reducing a design parameter of the Schrödinger operator, analyzing discrete spectra of the Schrödinger operator and combining the analysis of the discrete spectra to construct the image.

Abstract: A method and system can analyze, reconstruct, and/or denoise an image. The method and system can include interpreting a signal as a potential of a Schrödinger operator, decomposing the signal into squared eigenfunctions, reducing a design parameter of the Schrödinger operator, analyzing discrete spectra of the Schrödinger operator and combining the analysis of the discrete spectra to construct the image.