The present application includes a computer-implemented method for detecting a tamper event. The method includes a step of receiving, from one or more sensors on an integrated module, data indicating one or more of light, acceleration, magnetic field, rotation, temperature, pressure, humidity, and audio. The method also includes a step of transmitting, to a microcontroller/evaluation engine coupled to the integrated module, the received data from the one or more sensors. The method further includes a step of determining, via the microcontroller, the received data exceeding one or more configurable thresholds of the tamper event. The method even further includes a step of assigning, via the microcontroller, a score associated with each of the one or more configurable thresholds being exceeded by the received data. The method yet even further includes a step of collecting, via the microcontroller, an aggregate total based on the assigning step. The method still further includes a step of evaluating, via the controller, the aggregate total in view of a predetermined alarm threshold of the tamper event.

A system is controlled by solving a mixed-integer optimal control optimization problem using branch-and-bound (B&B) optimization that searches for a global optimal solution within a search space. The B&B optimization iteratively partitions the search space into a nested tree of regions, and prunes at least one region from the nested tree of regions before finding a local optimal solution for each region when a dual objective value of a projection of a sub-optimal dual solution estimate for each region into a dual feasible space is greater than an upper bound or lesser than a lower bound of the global optimal solution maintained by the B&B optimization.

The embodiments described herein include one embodiment that provides a control method that includes connecting a first controller to a control system; receiving control system configuration data from a database, in which the configuration data comprises holistic state data of a second controller in the control system; and configuring operation of the first controller based at least in part on the configuration data received.

A method and a system for detecting anomalies in a water distribution system comprises a network of nodes, and is equipped with sensors of at least water velocity at a subset of the nodes. The water distribution system is modeled by a hydraulic model. The method comprises parametrizing the hydraulic model with initial values of a set of control variables, using the sensors to obtain values of state variables of the network at the nodes, using the hydraulic model to calculate predicted values of state variables, recursively calculating the values of control variables which, applied to the hydraulic model, permit to obtain the predicted values of state variables the closest to the observed values, and classifying nodes of the network based on the values of control variables.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. The apparatus, engine, system and method may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may comprise an adaptor capable of pushing the received raw data to one or more databases to processed data; an extractor capable of extracting the processed data from the one or more databases; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model comprised of target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing at least a visualization of the feedback and of the performance.

A method for predicting energy consumption of a vehicle using a statistical model. The method includes (i) predicting a set of future input vectors for the vehicle at defined time intervals corresponding to a plurality of future points in time based on a subset of a plurality of reference input vectors previously generated at the defined time intervals at a plurality of previous points in time, (ii) predicting a change in the energy level of the vehicle at the future points in time using a processor and the statistical model, and (iii) providing results corresponding to the predicted change in the energy level to an output unit of the vehicle. The change in the energy level comprises a function of corresponding future input vectors and an associated weighting vector. The change in the energy level is predicted based on a regression analysis of the energy level associated with each of the reference input vectors.

An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The optimization system includes a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with maximum power consumption during a demand charge period. The high level optimization module is configured to optimize the objective function over the demand charge period.

An apparatus for the production of solid dosage forms is presented, wherein the apparatus comprises a material processing chamber which is operable for manufacturing a product according to a pre-set product formation process path. The apparatus has at least one sensor for continuously monitoring formation of the product in the material processing chamber during the product formation process non-invasively in real time by sensing at least one product functional attribute value and a means for comparing each sensed product functional attribute value with a desirable product functional attribute value for that point on the product formation process path. A controller controls operation of the material processing chamber in response to the sensed product functional attribute value for maintaining the product on the product formation process path.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. Included are a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may include an adaptor; an extractor; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model comprised of target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing a visualization of the feedback.

A system and method for energy optimization is disclosed. The system may collect information from an information collector data including energy usage and storage data of at least one renewable energy generation system and battery energy storage system (BESS). The system may identify historical events that result in curtailment of renewable energy production, determine whether there is a curtailment of renewable energy production based at least on one historical event supervise the charge and discharge cycles of the at least one BESS; and ensuring that the diesel generators minimum up/down time is satisfied based on controlling at least one parameter of the BESS.