An energy management system (EMS) that is augmented by Internet of Things (IoT) is introduced. Using IoT technology, real-time energy-consumption data can be efficiently collected and analyzed, leading to an improved awareness and evaluation of the energy consumption of manufacturing processes. The architecture and function of the IoT-based EMS are introduced. Because the existing monitoring standard for energy conservation defines only the minimum evaluation criterion, the result is either qualified or unqualified. Fully characterizing the energy consumption of industrial energy-intensive equipment is challenging. For this reason, this paper constructs a comprehensive evaluation model for monitoring industrial energy conservation. An evaluation index for industrial energy-intensive equipment is proposed. Then, by integrating an analytic hierarchy process and a fuzzy comprehensive evaluation method, a method is provided for an IoT-based industrial EMS to fully evaluate the operational level of energyintensive equipment. Finally, a case study is provided to verify the effectiveness of this method.

In this study, a modified Cahn–Hilliard equation with a very simple format was proposed, which can be used to simulate immiscible multi-component/multiphase flow with a super large density ratio. In addition, based on this modified equation and the Navier–Stokes equations, an improved multiphase lattice Boltzmann flux solver (IMLBFS) has been proposed, and its computational ability has been tested by multiple numerical examples, including Laplace law, two bubbles merging, contact angle, bubble rising, and droplet splashing on a thin film. The results show that the proposed IMLBFS can simulate immiscible two-phase flow with a very large density ratio up to 1:5000 or 1:10 000 under various operating conditions, including the Reynolds number reaching 10 000. In addition, IMLBFS also has excellent features such as clear physical properties, freely adjustable source term strength, and effective suppression of mass loss.