Energy saving potential analysis applying factory scale energy audit A case study of food production

An energy audit (EA) is a crucial step in boosting factory energy efficiency and obtaining certification for cleaner manufacturing. The results of a preliminary energy audit carried out at a sizable industrial facility in Jordan that creates some of the most well-known foods in the Middle East are presented in this study. The monthly demand of the factory for diesel ranged from 75,251.545 to 166,666.67 L. The factory energy model which is used to examine the impact of various energy-saving practices on the factory's primary energy consumption, was developed with the help of the energy audit. It has been established that optimizing the factory's energy use and the boiler systems' performance with regards to diesel consumption can withstand an expected monthly financial savings of 14205.85 Jordanian Dinar (JD). This has allowed a reduction in energy use of up to 18%. The CO₂ harmful emissions were also decreased. Additionally, it is estimated that switching from the proposed motors to energy-efficient motors will cost less overall over time, saving around 3472.314 JD/month or 0.33576/year on average. Moreover, it was discovered that a total of 772.82021 Ton CO₂/year emissions may be avoided each year.

Case-based reasoning based on grey-relational theory for the optimization of boiler combustion systems

Boiler combustion optimization is an important method to improve the flexibility of thermal power units and ensures the stability of unit operation. However, time-variability of boiler combustion systems and time-consuming optimization methods pose great challenges for the use of boiler combustion optimization techniques because many optimization methods cannot be used online in practical engineering due to time constraints. In this paper, we propose a case-based reasoning optimization method based on grey-relational theory (GR-CBR) for online optimization of a boiler combustion system. After the introduction of the proposed algorithm, we discuss the potential of applying the proposed GR-CBR optimization method to a boiler combustion system; a case study of an existing fossil fuel power plant is conducted to demonstrate the feasibility of the proposed method. A least-squares support vector machine (LS-SVM) model of the boiler combustion process is established by using the real-time operation data of a 350-MW coal-based power plant. Based on the model, a non-linear global optimization algorithm is proposed to obtain the optimal case base and real-time data mining and online optimization are used to achieve efficient and stable boiler combustion optimization. The results of combining offline optimization with online querying show that this approach is suitable for online real-time combustion optimization, and provides support for power plant operators for optimization and condition monitoring to improve boiler efficiency, reduce NOx emissions, and ensure stable and efficient operation of the power system.