Comprehensive energy modeling and optimization play a key role in sustainable development of complex petrochemical industries. However, it is difficult to make effective energy modeling and optimization due to the characteristics of uncertainty, high nonlinearity, and with noise of modeling data from the practical production. To deal with this problem, a novel energy modeling and efficiency optimization method using a novel extreme learning fuzzy logic network (ELFLN) is proposed. In the proposed method, Mamdani type fuzzy inference system (FIS) and multi-layer feedforward artificial neural network (MLFANN) are adopted. First, the fuzzy inference replaces the hidden layers of artificial neural network (ANN). Then the proposed framework takes fuzzy membership degrees instead of precise values as the output. Meanwhile, an extreme learning algorithm based on Moore-Penrose Inverse is utilized to train the network efficiently. Three levels of energy efficiency of “low efficiency, median efficiency and high efficiency” can be effectively achieved using the proposed method. For inefficiency samples, valid slack variables are predicted for finding the direction of improving the efficiency. The energy efficiency optimization performance and the practicality of the proposed method is confirmed through an application of China ethylene industry. Finally, the energy saving potential is indicted as 8.82% and practical ethylene production can be guided by the result of the demonstration analysis.

Inaccurate motor parameters can lead to an inefficient motor control. Although several motor estimation methods have been utilized to estimate motor parameters, it is still challenging to ensure a good level of confidence in the estimation. In this paper, we propose a novel offline induction motor parameter estimation method based on sparse grid optimization algorithm. The estimation is achieved by matching the response of machines mathematical model with recorded stator current and voltage signals. This approach is noninvasive as it uses external measurements, resulting in reduced system complexity and cost. A globally optimal point was found by sampling on the sparse grid, which was created using the hyperbolic cross points and additional heuristics. This has resulted in reducing the total number of search points, and provided the best match between the mathematical model and measurement data. The estimated motor parameters can be further refined by using any local search method. The experimental results indicate a very good agreement between estimated values and reference values.