A data-driven approach is utilized to model a chiller plant that has four chillers, four cooling towers, and two chilled water storage tanks. The chillers have varying energy efficiency. Since the chiller plant model derived from data-driven approach is nonlinear and non-convex, it is not practical to solve it by using the traditional gradient-based optimization algorithm. A two-level intellige...

Modeling and optimization of complex systems traditionally have required significant programming efforts in order to encode the model dynamics, the cost functions and the constraints in a format suitable for state of the art numerical algorithms. The availability of dedicated languages for physical modeling has made the design process simpler, but often tools have been limited to a particular o...

In this paper the control of a continuous linear plant where the state sensor is connected to a linear controller/actuator via a network is addressed. The work focuses on reducing the network usage using knowledge of the plant dynamics. Specifically, the controller uses an explicit model of the plant that approximates the plant dynamics and makes possible the stabilization of the plant even und...

The objective of our study was to simulation of carbon monoxide (CO) dispersion exited from the stacks of a cement industry of Doroud, Iran by Gaussian Model (GM). Four sampling period was conducted to measurement of CO from the factory's three-stack flow during one year. The input parameters for were the rate of CO emission, meteorological data, factors of related to the stack, and factors of ...

This study addresses a stochastic structure for generation companies (GenCoʼs) that participate in hydro-thermal self-scheduling with a wind power plant on short-term scheduling for simultaneous reserve energy and energy market. In stochastic scheduling of HTSS with a wind power plant, in addition to various types of uncertainties such as energy price, spinning /non-spinning reserve prices, unc...

Traditionally, Electrical power plant capacities are determined after specific plant locations have been selected. In this paper an expansion policy of power plant centers involving the choice of regions that must be allocated to power plant centers and power plant centers capacities over a specified planning horizon (years) is tackled. The problem is performed as a mixed integerprogramming mod...

Fires in the tallgrass prairie are frequent and significantly alter nutrient cycling processes. We evaluated the short-term changes in plant production and microbial activity due to fire and the long-term consequences of annual burning on soil organic matter (SOM), plant production, and nutrient cycling using a combination of field, laboratory, and modeling studies. In the short-term, fire in t...

Arsenic is one of the heavy metals and nearly all its compounds, especially organic compounds, are toxic. The wide spectrum of diseases caused by this element has led to evaluation of the toxicity of different arsenic species and identification of the major natural and anthropogenic pollution sources of it in the nature. Mining activities are among the main sources of anthropogenic pollution of...

This paper introduces an integrated design for realtime plant modeling at a copper mine, using Support Vector Machines (SVM), Neural Networks, and new technologies such as Machine-to-Machine (M2M) and Cloud Computing with an Android client. This solution was designed for a plant inside a copper mine which cannot tolerate interruption and for which their in situ modeling, in real time, is an ess...

Most data-based predictive modeling techniques have an inherent weakness in that they may give unstable or inconsistent results when the predictor data is highly correlated. Predictive modeling problems of this design are usually under constrained and are termed ill-posed. This paper presents several examples of ill-posed surveillance and diagnostic problems and regularization methods necessary...