The design of energy systems has become an issue of worldwide interest. A single optimal energy system cannot be suggested because the availability of infrastructure and resources, and the acceptability of the system should be discussed locally with the involvement of all related stakeholders. A simulation-based approach is strongly needed for such a purpose. In this paper, the current major trends are reviewed on research activities related to smart energy systems. First, a bibliometric analysis of academic papers was conducted with the aim of visualizing clusters that indicate the hot topics attributable to smart energy systems. Considering the extracted research topics and representative references that are the core of each cluster, possible simulation-based approaches for the design of smart energy system are schematically structured on the basis of social, economic, and technological aspects. Through this study, the framework needed for the design of smart energy systems is characterized with simulation-based socioeconomic and technological analyses.
In the present research, the critical pressures of organic compounds were selected as a model case and were predicted using a quantitative structure-property relationship model. The coverage of prediction contains hydrocarbons and non-hydrocarbon organic compounds containing O, S, and N atoms. In total, 802 hydrocarbons and 1144 non-hydrocarbon organic compounds were used to develop a model with the 3D structure of each compound being optimized by quantum mechanical calculations. Furthermore, appropriate descriptors to explain critical pressure effectively were selected by forward selection regression and genetic algorithm. Multi-linear regression and neural networks were used to establish prediction models for the hydrocarbon and non-hydrocarbon organic compounds. The prediction models achieved sufficiently high performances, with R2>0.96. This research also analyzes implications of selected descriptors, and the relationship between the descriptor and critical pressure.
In order to intensify a phase transfer catalytic reactor with the third liquid phase, it is necessary to optimize multiple operation variables that are concerned with the mass transfer rate. In the present paper, we propose a systems approach by application of a rate based model for mass transfer between two phases. Application of the developed rate based model performs simulation with higher accuracy than the conventional partition equilibrium model, for numerical analysis of behavior of a batch process for the synthesis of 4-benzyloxy-vanillin by using tetrabutylammonium bromide as the phase transfer catalyst. Moreover, influences of the rotational speed of the impeller on changes in the overall mass transfer coefficients are analyzed by using estimation equations for the Sauter diameter and the specific interfacial area of the dispersed phase. Consequently, it is found that the assumption of spherical droplets for the dispersed phase is not applicable in the application of the estimation equations. Then, the sensitivity analysis based on the rate based model demonstrates that characteristics of liquid–liquid dispersion in the agitated triphase reactor is complex due to the role of the third phase as a surfactant.
In the present paper, iterative learning control (ILC) is integrated with a model predictive control (MPC) technique to reject real-time disturbances. The proposed scheme is called iterative learning model predictive control (ILMPC). ILC is an effective control technique for batch processes, but it is not a real-time feedback controller. Thus, it should be combined with MPC for real-time disturbance rejection. The existing ILMPC techniques make the error converge to zero. However, if the error converges to zero, an impractical input trajectory may be calculated. We use a generalized objective function to independently tune weighting factors of manipulated variable change with respect to both the time index and batch horizons. If the generalized objective function is used, output error converges to non-zero values. We provide convergence analysis for both cases of zero convergence and non-zero convergence.
Multivariate statistical process control (MSPC) is important for monitoring multiple process variables and their relationships while controlling chemical and industrial plants efficiently and stably. Although many MSPC methods have been developed to improve the accuracy of fault detection, noise in the operating data, such as measurement noise and sensor noise, conceals important variations in process variables. This noise makes it difficult to recognize process states, but has not been fully considered in traditional MSPC methods. In this study, to improve the process state recognition performance, we apply several smoothing methods to each process variable. The best smoothing method and its hyperparameters are selected based on the normal distribution and variation of the reduced noise. Through case studies using numerical data and dynamic simulation data from a virtual plant, it is confirmed that the fault detection and identification accuracy are improved using the proposed method, which leads to enhanced state recognition performance.
Early detection of anomalies is crucial to maintain high productivity at a refinery. For this purpose, we propose an anomaly detection system based on adaptive resonance theory (ART) for industrial plants. The feature of the system is that it has several ART systems applied to subsystems of the plant in order to narrow down the cause of the anomalies. This report presents our examination of online anomaly detection tests on whether or not the proposed system is applicable to a distillation tower system. The tests were conducted with experimental equipment of a distillation tower in Universiti Teknologi PETRONAS (UTP). In the tests, we carried out four cases of anomaly operation (e.g., valve sticking and tray upset) that would cause quality or yield losses in the product. By learning normal operation data, the proposed system could detect anomalies in all four cases, and no false positives were observed in normal operation. We also found that the system could narrow down the cause of the anomalies by using the results of each ART system, thereby demonstrating that the system is applicable for the distillation tower system.
A plant alarm system provides critical information to operators as the third layer of independent protection layers when a chemical plant is undergoing an abnormal situation. Therefore, methods for designing plant alarm systems are crucial for plant safety. Because plants are maintained as part of the plant lifecycle, plant alarm systems should be properly managed and adapted to changes that occur throughout the lifecycle. To manage changes, the design rationale of the plant alarm system should be explained explicitly. In the present paper, we explain a design problem for plant alarm systems and propose a method for designing alarm systems by using differential and algebraic equations, cause-effect matrices, and preference indices.
Sequential alarms, which are sets of alarms occurring sequentially within a short period of time after an initial alarm warning of an abnormality, reduce the ability of plant operators to cope with operation abnormalities because critical alarms are often buried under numerous correlated alarms. We have improved a previously proposed dot matrix method for identifying sequential alarms hidden in noisy plant-operation data by adding the use of a sliding window. The alarm sequence in the plant-operation data is converted into a set of windows containing adjacent alarms. All combinations of windows are compared, and repeated windows are identified on the basis of a minimal number of alarm matches. The alarms in each repeated window comprise a sequential alarm. Application of this method to simulated operation data for an azeotropic distillation column demonstrated that it can identify sequential alarms in noisy plant-operation data. Classifying such alarms into small numbers of subsequences effectively reduces the number of sequential alarms, enabling engineers to reduce unnecessary alarms related to plant operations.
Porous titania (TiO2) particles were prepared using a sol–gel technique employing Alkylketene Dimers (AKD) as a template. The effects of incremental additions of AKD as a template material on crystal structure, surface and internal morphology, and pore properties were investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and nitrogen adsorption/desorption. The prepared titania had more than one pore size on its surface and internally. Incremental addition of AKD caused an increase in specific surface area. The prepared porous titania with the addition of AKD at 5 wt% exhibited a high specific surface area of 124 m2/g, which is markedly higher than in titania without AKD (53.5 m2/g). To quantitatively evaluate the porous structure, we performed fractal analysis over a wide scale using box-counting, small X-ray scattering (SAXS) and the FHH (Frenkel–Halsey–Hill) method. Porous titania showed fractal properties, both on its surface and internally.
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