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The evolution of the power industry toward large-scale automation and selfmonitoring provides the opportunity to optimize the technical and environmental performance of the plant with data-driven methods with little changes in infrastructure. This article applies the artificial neural network (ANN) and genetic algorithm (GA) to predicting and optimizing NO emissions. Multiple linear regression models, correlation matrix, and research background are employed to find the most influential input features. The generated power, natural gas flow, the flow of gas recirculation fan, gas air heater temperature, and the amounts of oxygen in the stack are identified as the effective input features. Mean Square Error (MSE) and the coefficient of determination (R 2 ) of best architecture (22 neurons in a hidden layer) are calculated 0.0117 and 0.96651, respectively. The Average Percentage Error (APE) is usually below 10%, meaning the model is in good agreement with real data. Finally, the Genetic Algorithm (GA) is used to minimize the amount of NO emissions. The ANN-GA techniques reduce the NO emission by at least 32% for selected records, enabling us to optimally find the prominent features affecting NO emission by the operational conditions and low economic costs.

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Entropy is producing during any irreversible process. In the cancer cells, the entropy generation measures the irreversibility; so, the cancer cells have higher entropy generation than the healthy cells. The entropy generation rate shows the amount of robustness, progression, and invasion of the cancer cells. From a thermodynamic aspect, cancer's origin and growth is an irreversible process, and the thermodynamic variables such as the cell volume, temperature, and entropy will change during this process. In this paper, a procedure based on experimental data is proposed to calculate dynamic entropy generation in the tumoral tissues by dynamic thermography and measurement of tumor size. The dynamic changes in the volume, temperature, and entropy associated with tumor cells over time are tested and evaluated in this regard. An in vivo assay has been developed to measure and analyze these changes. This assay investigated the growth of 4T1 Breast Tumor in 55 BALB/c mice over time. Infrared thermography has been employed to evaluate dynamic temperature changes of the tumors. The computer code has been developed to gather important data from tumoral and healthy mice's images to compute considered temperature differences and entropy generation associated with tumoral tissues. To better evaluate tumor tissue, the Micro PET Images are used to verify volume changes of tumors. The relation between the volume and temperature gradient of tumor cells has detected by measuring during the experiment. The entropy of tumor cells was studying and calculating during the process of tumor changes. Results show that entropy generation as the main concept of thermodynamic is a strong tool for the analysis of cancer cells and has a strong relationship with cancer growth.