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  • 1.
    Leva, Alberto
    et al.
    Politecn Milan, DEIB, I-20133 Milan, Italy..
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Seva, Silvano
    Politecn Milan, DEIB, I-20133 Milan, Italy..
    Cimino, Chiara
    Politecn Milan, DEIB, I-20133 Milan, Italy..
    Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection2019In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 58, no 51, p. 23211-23224Article in journal (Refereed)
    Abstract [en]

    In the process control, many PID loops are primarily devoted to rejecting load disturbances, and some of them are crucial for the quality of the overall plant operation. In such a scenario, automatic tuning is highly desired. However, load disturbance rejection calls for strong feedback up to quite high frequencies with respect to the dominant plant dynamics, on which most tuning rules are centered. As such it is difficult for a rule to yield good and, above all, uniform results in the face of all the various process structures it can be confronted with. In this paper, we propose an explicit model-based PID tuning rule specifically targeted at the problem just evidenced. The rule minimizes the magnitude of the nominal disturbance-to-output frequency response, at the same time preventing that magnitude to exhibit a peak or a plateau around its maximum. This characteristic, together with tuning the PID derivative filter, leads to sharp disturbance rejection without incurring in an excessive control sensitivity to high-frequency measurement noise and mitigates the problems caused by heterogeneous process dynamics. The proposed approach is assessed by comparing the rule with selected counterparts, on a literature benchmark with different process structures. A laboratory experiment is finally presented to show that our rule can withstand real-world operating conditions.

  • 2.
    Stangeland, Kristian
    et al.
    University of Stavanger.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Yu, Zhixin
    Universitetet i Stavanger.
    Thermodynamic Analysis of Chemical and Phase Equilibria in CO2 Hydrogenation to Methanol, Dimethyl Ether, and Higher Alcohols2018In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 57, no 11, p. 4081-4094Article in journal (Refereed)
    Abstract [en]

    CO2 hydrogenation can lead to the formation of various products, of which methanol, dimethyl ether (DME) and ethanol have received great attention. In this study, a comprehensive thermodynamic analysis of CO2 hydrogenation in binary (methanol/CO) and ternary product systems (methanol/CO with DME or ethanol) is conducted in Aspen Plus by the Gibbs free energy minimization method combined with phase equilibrium calculations. It is demonstrated that product condensation can be utilized to circumvent thermodynamic restrictions on product yield. Significant improvements in CO2 conversion can be achieved by operating at conditions favorable for product condensation, whereas the selectivity is mildly affected. The relevance of the results herein is discussed with regards to recent advances in catalysis and process design for CO2 hydrogenation. Our study highlights the importance of obtaining a thorough understanding of the thermodynamics of CO2 hydrogenation processes, which will be critical for developing potential breakthrough technology applicable at the industrial scale.

  • 3.
    Yang, J.
    et al.
    East China University of Science and Technology, Shanghai, China.
    Yu, X.
    East China University of Science and Technology, Shanghai, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering. Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Tu, S. -T
    East China University of Science and Technology, Shanghai, China.
    CO2 capture using amine solution mixed with ionic liquid2014In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 53, no 7, p. 2790-2799Article in journal (Refereed)
    Abstract [en]

    It is a focus to reduce the energy consumption and operating cost of CO2 capture from low-pressure flue gas streams of power plants using an aqueous amine-based absorbent. In this study, CO2 capture experiments were conducted in an absorption-desorption loop system using amine-based absorbents. The gas mixture containing CO2, O 2, SO2, and N2 in the composition range of flue gas from coal-fired power plant after flue gas desulfurization was selected as the feed gas. For an aqueous amine solution, the largest contribution to monoethanolamine (MEA) loss was made by evaporation during desorption, followed by the formation of sulfate and heat-stable salts. To reduce MEA loss and meanwhile decrease the energy consumption during CO2 desorption, an aqueous amine solution mixed with ionic liquid (30 wt % MEA + 40 wt % [bmim][BF4] + 30 wt % H2O) was proposed. The energy consumption of the mixed ionic liquid solution for absorbent regeneration was 37.2% lower than that of aqueous MEA solution. The MEA loss per ton of captured CO2 for the mixed solution was 1.16 kg, which is much lower than that of 3.55 kg for the aqueous amine solution. No ionic liquid loss was detected. In addition, the mixed ionic liquid solution showed a low viscosity of 3.54 mPa s at 323 K, indicating that the ionic liquid disadvantage of high viscosity can be overcome for absorbent delivery of CO2 capture. 

  • 4.
    Zhou, Y.
    et al.
    East China University of Science and Technology.
    Li, S.
    East China University of Science and Technology.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning2019In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 58, no 9, p. 3782-3796Article in journal (Refereed)
    Abstract [en]

    This paper proposes a boosting vine copula-based dependence description (BVCDD) method for multivariate and multimode process monitoring. The BVCDD aims to improve the standard vine copula-based dependence description (VCDD) method by establishing an ensemble of submodels from sample directions based on a boosting strategy. The generalized Bayesian inference-based probability (GBIP) index is introduced to assess the degrees of a VCDD model (submodel) to depict different samples, which means how likely an observation is under the probabilistic model for the system. Every sample is weighted individually according to the depiction degree. The weights are then used to choose a certain number of samples for each succeeding submodel. In this way, the samples with large error in the preceding model can be selected for training the next submodel. Moreover, the number of submodels as well as the number of training samples chosen for every submodel are determined adaptively in the ensemble learning process. The proposed BVCDD method can not only solve weak sample problems but also remove redundant information in samples. To examine the performance, empirical evaluations have been conducted to compare the BVCDD method with some other state-of-the-art methods in a numerical example, the Tennessee Eastman (TE) process, and an acetic acid dehydration process. The results show that the developed BVCDD models are superior to those obtained by the counterparts on weak samples in both accuracy and stability. 

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