A data-driven model for maximization of methane production in a wastewater treatment plant

A data-driven approach for maximization of methane production in a wastewater treatment plant is presented. Industrial data collected on a daily basis was used to build the model. Temperature, total solids, volatile solids, detention time and pH value were selected as parameters for the model construction. First, a prediction model of methane production was built by a multi-layer perceptron neural network. Then a particle swarm optimization algorithm was used to maximize methane production based on the model developed in this research. The model resulted in a 5.5% increase in methane production.     

Design of parts and manufacturing systems for reliability and maintainability

The design of parts and manufacturing systems can be viewed from different perspectives. This paper discusses the design of parts and manufacturing systems from reliability and maintainability perspectives. A convenient method of considering reliability and maintainability is through the use of design rules which are based on empirical and theoretical knowledge. Four design rules for improvement of reliability and maintainability of systems are presented. The design rules are proven and substantiated with numerical results. The relationship between the design for reliability and maintainability rules and design for manufacturing process rules is discussed.     

Extreme Engineering:Polarization in Product Development and Manufacturing

1.Introduction Products and solutions are usually developed to meet customer needs at a cost justified by the market.New technologies are likely to be adopted b...     

Reheat optimization of the variable-air-volume box

A data-driven approach for optimizing the reheat process in a variable-air-volume box is presented. Data-mining algorithms derive temporal predictive models from the reheat process data. The bi-objective model formed is solved with a modified particle swarm optimization algorithm. To increase computational efficiency, two levels of non-dominated solutions are introduced while solving the optimization model. A model predictive control strategy is used to generate controls minimizing the reheat output while maintaining the thermal comfort at an acceptable level.     

The prediction and diagnosis of wind turbine faults

The rapid expansion of wind farms has drawn attention to operations and maintenance issues. Condition monitoring solutions have been developed to detect and diagnose abnormalities of various wind turbine subsystems with the goal of reducing operations and maintenance costs. This paper explores fault data provided by the supervisory control and data acquisition system and offers fault prediction at three levels: (1) fault and no-fault prediction; (2) fault category (severity); and (3) the specific fault prediction. For each level, the emerging faults are predicted 5–60 min before they occur. Various data-mining algorithms have been applied to develop models predicting possible faults. Computational results validating the models are provided. The research limitations are discussed.     

Design of wind farm layout for maximum wind energy capture

Wind is one of the most promising sources of alternative energy. The construction of wind farms is destined to grow in the U.S., possibly twenty-fold by the year 2030. To maximize the wind energy capture, this paper presents a model for wind turbine placement based on the wind distribution. The model considers wake loss, which can be calculated based on wind turbine locations, and wind direction. Since the turbine layout design is a constrained optimization problem, for ease of solving it, the constraints are transformed into a second objective function. Then a multi-objective evolutionary strategy algorithm is developed to solve the transformed bi-criteria optimization problem, which maximizes the expected energy output, as well as minimizes the constraint violations. The presented model is illustrated with examples as well as an industrial application.     

Modeling and optimization of HVAC energy consumption

A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%.     

Expert systems and optimization

A problem-solving approach involving the integration of expert systems and optimization techniques is presented. A class of expert systems called tandem expert systems is introduced. Three variants of the tandem expert system, the data-reducing, model-based, and model-modifying expert systems, are explained with examples. It is emphasized that optimization techniques can be used more frequently in future expert systems     

Optimal Selection of Machinable Volumes

A brief review of the process planning literature is provided. One of the basic issues in process planning is the feature recognition. In the paper rather than representing a part with features, the representation using machinable volumes is emphasized. A model for the selection of machinable volumes with minimum corresponding machining costs, fixture and tool utilization costs is formulated. The model is illustrated with a numerical example. Computational results for five existing parts are presented.