The Kingdom of Saudi Arabia is known for its extreme climate where temperatures can exceed 50 °C, especially in summer. Improving agricultural production can only be achieved using innovative environmentally suitable solutions and modern agricultural technologies. Using Internet of Things (IoT) technologies in greenhouse farming allows reduction of the immediate impact of external climatic conditions. In this paper, a highly scalable intelligent system controlling, and monitoring greenhouse temperature using IoT technologies is introduced. The first objective of this system is to monitor the greenhouse environment and control the internal temperature to reduce consumed energy while maintaining good conditions that improve productivity. A Petri Nets (PN) model is used to achieve both monitoring of the greenhouse environment and generating the suitable reference temperature which is sent later to a temperature regulation block. The second objective is to provide an Energy-Efficient (EE) scalable system design that handles massive amounts of IoT big data captured from sensors using a dynamic graph data model to be used for future analysis and prediction of production, crop growth rate, energy consumption and other related issues. The design tries to organize various possible unstructured formats of raw data, collected from different kinds of IoT devices, unified and technology-independent fashion using the benefit of model transformations and model-driven architecture to transform data in structured form.

One of the vital processes that should be monitored and analyzed continuously in the oilgas and petroleum-related industries is the multi-phase flow inside pipes. Multi-phase flow means flowing two or more phases of gas, liquid, or solid inside a pipe. Electrical Capacitance Tomography (ECT) is a feasible and economical solution for monitoring dynamic applications. The ECT system offers the benefits of no radiation, non-intrusive, and non-invasive. Despite its potential, ECT systems deployment's major limitation is the crucial need to develop rapid image reconstruction algorithms. In this paper, a Local Ensemble Transform Kalman Filter (LETKF) is developed as a non-linear system estimator for reconstructing images in the ECT system. This method manages each node of the model independently by assimilating only the observations at a predefined distance. The localized approach of the LETKF gives it high computational efficiency allowing it to be applied to large dynamic systems. A quantitative analysis using Image Error (IE) and Coefficient Correlation (CC) measures has been applied to prove the effectiveness of the proposed algorithm. Indeed, the IE has been significantly decreased (around 62%), and the CC greatly increased (around 58%). Then, the influence of the noise was discussed. The results are promising and prove the algorithm feasibility.INDEX TERMS ECT, image reconstruction, Kalman filter, multi-phase flow.

No abstract

This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior.