A machine is configured to grind and/or shred a waste material and to mix the waste material with a filler material so as to dilute the waste material to a desired level, such as on a minority basis by weight. The machine also includes a blender that grinds or shreds the waste material and the filler material received from drum while mixing the materials together. Materials may be supplied to the blender by a drum located above the blender and/or a hopper located remote from the blender and coupled to the blender by a conveyor. The waste material may be rendered unusable and unrecognizable in the resultant mixture. A computer system can store the various measurements in an internal memory and/or transmit the various measurement to a remote server. Information that supports a finding of compliance with a given regulation can be transmitted to relevant parties.

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.

A method monitors the energy cost for the production of a product lot using a manufacturing execution system (MES) that enables the operator of a production facility to optimize the production process in terms of energy costs. The method includes a) executing a production process being scheduled and controlled by the MES to produce the product lot; b) for each individual production step measuring the energy consumption over the course of the execution of the individual production step; c) creating a data model within the MES that correlates production specific data and the energy consumption data related to the product lot; d) defining commands to manage the production specific data and the energy consumption data wherein the commands are web APIs; and e) evaluating the production specific data and the energy consumption data and creating an energy consumption profile for the production process related to the product lot.

A controller for equipment obtains utility rate data indicating a price of one or more resources consumed by the equipment to serve energy loads. The controller generates an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the equipment at each of a plurality of time steps. The controller optimizes the objective function to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps. Load equality constraints on the objective function ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps. The controller operates the equipment to achieve the distribution of the predicted energy loads at each of the plurality of time steps.

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 high level optimization module is configured to optimize the objective function over the optimization period subject to load equality constraints and capacity constraints on the central plant equipment to determine an optimal distribution of the building energy loads over multiple groups of the central plant equipment.

A production machine (1) that has at least two different standby states is operated. Incoming and outgoing flows (ES, AS), comprising energy flows (EN) and material flows (ST), occur in each standby state, depending on the state. Each standby state has an associated base rating (BB1, BB2) that has a dependency on the period of time that elapses when changing from the respective standby state to the productive state of the machine (1). The base rating (BB1, BB2) is higher, the shorter said period of time is.

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 the optimization period as a function of the utility rate data and an amount of the one or more resources consumed by multiple groups of the central plant equipment. The optimization system includes a load change penalty module configured to modify the objective function to account for a load change penalty resulting from a change in an amount of the building energy loads assigned to one or more of the groups of central plant equipment.

Systems and methods for low level central plant optimization are provided. A controller for the central plant uses binary optimization to determine one or more feasible on/off configurations for equipment of the central plant that satisfy operating constraints and meet a thermal energy load setpoint. The controller determines optimum operating setpoints for each feasible on/off configuration and generates operating parameters including at least one of the feasible on/off configurations and the optimum operating setpoints. The operating parameters optimize an amount of energy consumed by the central plant equipment. The controller outputs the generated operating parameters via a communications interface for use in controlling the central plant equipment.

A method of manufacturing at least a first product and a second product with at least a first machine and a second machine at minimum cost in an environment in which a cost of energy used by the first machine and the second machine varies as a function of time may include generating multiple chromosomes, determining fitness scores of each of the chromosomes, randomly generating, with probabilities based on the fitness scores, new chromosomes, determining fitness scores of the new chromosomes, selecting one of the new chromosomes with an optimal fitness score, and manufacturing at least the first product and the second product with at least the first machine and the second machine according to a schedule based on the selected new chromosome.

A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.