On the theory of economic MPC: ELOC and approximate infinite horizon EMPC

In recent years, the notion of Economic Model Predictive Control (EMPC) has gained significant interest. Despite a marked improvement in economic performance, it has been shown that this performance will degrade substantially if implemented with a horizon that is not sufficiently large. In the current effort, it is shown that if applied to a particular reaction process, EMPC performance will abruptly collapse at a critical horizon size. To alleviate this issue, we develop an Infinite Horizon EMPC (IH-EMPC) formulation. While this IH-EMPC problem is computationally intractable, it does lead to an approximation of the optimal policy. The resulting Approximate IH-EMPC (AIH-EMPC) is identical to the original finite horizon EMPC, but includes a final cost term that represents the objective function from the finite horizon to infinity. With two example systems, a chemical reactor and a power system with energy storage, it is shown that the AIH-EMPC policy is virtually insensitive to its computational horizon size.     

Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework

Predictive maintenance is one of the important technical means to guarantee and improve the normal industrial production. The existing bottlenecks for popularization and application are analyzed. In order to solve these problems, a cooperative awareness and interconnection framework across multiple organizations for total factors that affect prediction maintenance decision-making is discussed. Initially, the structure and operation mechanism of this framework are proposed. It is designed to support the sharing of data, knowledge and resources. As a key supporting technology, the digital twin is also integrated into it to improve the accuracy of fault diagnosis and prediction and support making a maintenance plan with higher accuracy and reliability. Then, under this framework, an integrated mathematical programming model is established with considering the parameter uncertainty and an NSGA-II hybrid algorithm is utilized to solve it. Moreover, an adjustment strategy for a maintenance plan is discussed in response to the dynamic characteristics of the actual maintenance environment. Finally, a case, prediction maintenance decision-making for bearings in grinding rolls of the large vertical mill, is studied. Analysis results verify the advantage of the integrated solving mechanism based on the proposed framework. The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises.     

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique

In order to ensure safe and sustainable design of geosynthetic-reinforced soil foundation (GRSF), settlement prediction is a challenging task for practising civil/geotechnical engineers. In this paper, a new hybrid technique for predicting the settlement of GRSF has been proposed based on the combination of evolutionary algorithm, that is, grey-wolf optimisation (GWO) and artificial neural network (ANN), abbreviated as ANN-GWO model. For this purpose, the reliable pertinent data were generated through numerical simulations conducted on validated large-scale 3-D finite element model. The predictive power of the model was assessed using various well-established statistical indices, and also validated against several independent scientific studies as reported in literature. Furthermore, the sensitivity analysis was conducted to examine the robustness and reliability of the model. The results as obtained have indicated that the developed hybrid ANN-GWO model can estimate the maximum settlement of GRSF under service loads in a reliable and intelligent way, and thus, can be deployed as a predictive tool for the preliminary design of GRSF. Finally, the model was translated into functional relationship which can be executed without the need of any expensive computer-based program.     

Impact of Ozonation Process on the Microbiological Contamination and Antioxidant Capacity of Highbush Blueberry (Vaccinum corymbosum L.) Fruit during Cold Storage

In the studies conducted, the impact of the innovative ozonation procedure on the microbial state and antioxidant potential of highbush blueberry (Vaccinum corymbosum L.) stored under cold storage conditions was assessed. Microbiological analysis was carried out to determine the total number of mesophilic aerobic bacteria and the total number of fungi during the storage experiment. In addition, changes in the flavonoid, anthocyanins, and vitamin C content and the total antioxidant capacity were monitored during the storage. The degree of fruit infection with gray mold and anthracnose was determined. It was found that daily ozonation of fruits with a dose of 15 ppm for 30 min, every 12 h, for 28 days effectively reduced the development of aerobic mesophilic bacteria and fungi. On the last day of storage, symptoms of the infection by gray mold were observed in 27.5% of the control fruit, while the absence of symptoms was observed in case of the ozonated fruit. On the other hand, ozone was ineffective in case of inhibiting the infection by anthracnose. Nevertheless, the ozonation process allowed maintaining a high antioxidant potential of the fruit and substantially reduced losses of flavonoids, anthocyanins, and vitamin C. The utilized procedure has proved to be effective, providing the possibility of extensive use of ozone as a factor allowing sustaining a high commercial and consumption value of the fruit over extended time.     

Insights into discrepancy in power generation among glucose and malate grown Rubrivivax benzoatilyticus JA2 microbial fuel cells

In this study, Rubrivivax benzoatilyticus JA2^T (=ATCC BAA-35^T = JCM 13220^T = MTCC 7087^T), an anoxygenic photosynthetic bacterium, was subjected to altered conditions and observed for changes in power outcome in the two chambered microbial fuel cells (MFCs), the basis of which was established using metabolomic studies. This is an extension to our previous studies, which showed that, under photo heterotrophic conditions, glucose in the form of a solitary carbon resource in minimal media, caused the strain JA2 to exhibit altered growth rates, progressive loss of pigmentation and reduced cell size (3–4 μm), compared to malate grown cells (6–7 μm). When R. benzoatilyticus JA2 cells were grown in malate bio-anodes, they presented higher potentials (289.22 ± 4.6 mV or 436.22 OCV per mg dry weight) compared to glucose bioanodes (163 ± 5.5 mv or 188.98 OCV per mg dry weight). Insights from the metabolomic footprints and fingerprints have revealed differential regulation of key components in the central metabolic pathway such as fumarate, citrate and succinate, which are significantly increased in malate grown bio anodes. Strain JA2 cells when grown with malate as substrate are densely grown on the electrodes and exhibited reduced size, when observed under SEM, which contrasts with control cells grown on malate broth. The artificial selection pressure of the MFC and the different metabolic pathways followed by these bacteria are the reasons for such discrepancy in the power production by the strain JA2. These adaptations may indicate survival advantage during the electron transfer and growth in bio anodes. The study throws light on what types of effluents would be more suitable as substrates for R. benzoatilyticus JA2 microbial fuel cells.     

Asphaltene precipitation and deposition in oil reservoirs – Technical aspects,experimental and hybrid neural network predictive tools

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO₂ injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.     

应用型本科《微生物学》课程目标定位及任务驱动式教学

应用型本科是高等教育转型发展的产物,对普通本科教育是一种必要的补充。本文分析了《微生物学》教学工作者在应用型本科的前提下如何进行准确的课程目标定位,并探索了与之适应的任务驱动式教学模式。结果表明:任务驱动式教学能有效执行应用型本科《微生物学》课程目标定位,极大提高学生学习的积极性和主动性,取得良好的教学效果力。