Image1_A process-model-free method for model predictive control via a reference model-based proportional-integral-derivative controller with application to a thermal power plant.pdf

Introduction: Model predictive control (MPC) is an advanced control strategy which can achieve fast reference tracking response and deal with process constraints, time delay and multivariable problems. However, thermal processes in coal-fired power plants are usually difficult to model accurately, which limits the application of MPC to thermal power plants.Methods: To solve the problem, this paper proposes a process-model-free method for MPC via a reference model (RM)-based controller, i.e., a desired dynamic equational (DDE) proportional-integral-derivative (PID) controller (DDE-PID).Results and Discussion: The DDE-PID can provide the design model and enhance the disturbance rejection ability for MPC. Simulations and results of field tests on a coal-fired unit show the superiorities of the proposed controller in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of the field application of the MPC with DDE-PID, or MPC-DDE in short, to thermal power plants.</p

Table2_A process-model-free method for model predictive control via a reference model-based proportional-integral-derivative controller with application to a thermal power plant.docx

Introduction: Model predictive control (MPC) is an advanced control strategy which can achieve fast reference tracking response and deal with process constraints, time delay and multivariable problems. However, thermal processes in coal-fired power plants are usually difficult to model accurately, which limits the application of MPC to thermal power plants.Methods: To solve the problem, this paper proposes a process-model-free method for MPC via a reference model (RM)-based controller, i.e., a desired dynamic equational (DDE) proportional-integral-derivative (PID) controller (DDE-PID).Results and Discussion: The DDE-PID can provide the design model and enhance the disturbance rejection ability for MPC. Simulations and results of field tests on a coal-fired unit show the superiorities of the proposed controller in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of the field application of the MPC with DDE-PID, or MPC-DDE in short, to thermal power plants.</p

Table1_A process-model-free method for model predictive control via a reference model-based proportional-integral-derivative controller with application to a thermal power plant.docx

Introduction: Model predictive control (MPC) is an advanced control strategy which can achieve fast reference tracking response and deal with process constraints, time delay and multivariable problems. However, thermal processes in coal-fired power plants are usually difficult to model accurately, which limits the application of MPC to thermal power plants.Methods: To solve the problem, this paper proposes a process-model-free method for MPC via a reference model (RM)-based controller, i.e., a desired dynamic equational (DDE) proportional-integral-derivative (PID) controller (DDE-PID).Results and Discussion: The DDE-PID can provide the design model and enhance the disturbance rejection ability for MPC. Simulations and results of field tests on a coal-fired unit show the superiorities of the proposed controller in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of the field application of the MPC with DDE-PID, or MPC-DDE in short, to thermal power plants.</p

Table4_A process-model-free method for model predictive control via a reference model-based proportional-integral-derivative controller with application to a thermal power plant.docx

Introduction: Model predictive control (MPC) is an advanced control strategy which can achieve fast reference tracking response and deal with process constraints, time delay and multivariable problems. However, thermal processes in coal-fired power plants are usually difficult to model accurately, which limits the application of MPC to thermal power plants.Methods: To solve the problem, this paper proposes a process-model-free method for MPC via a reference model (RM)-based controller, i.e., a desired dynamic equational (DDE) proportional-integral-derivative (PID) controller (DDE-PID).Results and Discussion: The DDE-PID can provide the design model and enhance the disturbance rejection ability for MPC. Simulations and results of field tests on a coal-fired unit show the superiorities of the proposed controller in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of the field application of the MPC with DDE-PID, or MPC-DDE in short, to thermal power plants.</p

Table3_A process-model-free method for model predictive control via a reference model-based proportional-integral-derivative controller with application to a thermal power plant.docx

Introduction: Model predictive control (MPC) is an advanced control strategy which can achieve fast reference tracking response and deal with process constraints, time delay and multivariable problems. However, thermal processes in coal-fired power plants are usually difficult to model accurately, which limits the application of MPC to thermal power plants.Methods: To solve the problem, this paper proposes a process-model-free method for MPC via a reference model (RM)-based controller, i.e., a desired dynamic equational (DDE) proportional-integral-derivative (PID) controller (DDE-PID).Results and Discussion: The DDE-PID can provide the design model and enhance the disturbance rejection ability for MPC. Simulations and results of field tests on a coal-fired unit show the superiorities of the proposed controller in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of the field application of the MPC with DDE-PID, or MPC-DDE in short, to thermal power plants.</p

Highly Sensitive Surface-Enhanced Raman Scattering Sensing of Heparin Based on Antiaggregation of Functionalized Silver Nanoparticles

We report a simple and sensitive surface-enhanced Raman scattering (SERS) platform for the detection of heparin, based on antiaggregation of 4-mercaptopyridine (4-MPY) functionalized silver nanoparticles (Ag NPs). Here, protamine was employed as a medium for inducing the aggregation of negatively charged 4-MPY functionalized Ag NPs through surface electrostatic interaction, which resulted in significantly enhanced Raman signal of the Raman reporter. However, in the presence of heparin, the interaction between heparin and protamine decreased the concentration of free protamine, which dissipated the aggregated 4-MPY functionalized Ag NPs and thus decreased Raman enhancement effect. The degree of aggregation and Raman enhancement effect was proportional to the concentration of added heparin. Under optimized assay conditions, good linear relationship was obtained over the range of 0.5–150 ng/mL (<i>R</i>² = 0.998) with a minimum detectable concentration of 0.5 ng/mL in standard aqueous solution. Furthermore, the developed method was also successfully applied for detecting heparin in fetal bovine serum samples with a linear range of 1–400 ng/mL

Controlled Intercalation and Chemical Exfoliation of Layered Metal–Organic Frameworks Using a Chemically Labile Intercalating Agent

Creating ordered two-dimensional (2D) metal–organic framework (MOF) nanosheets has attracted extensive interest. However, it still remains a great challenge to synthesize ultrathin 2D MOF nanosheets with controlled thickness in high yields. In this work, we demonstrate a novel intercalation and chemical exfoliation approach to obtain MOF nanosheets from intrinsically layered MOF crystals. This approach involves two steps: first, layered porphyrinic MOF crystals are intercalated with 4,4′-dipyridyl disulfide through coordination bonding with the metal nodes; subsequently, selective cleavage of the disulfide bond induces exfoliation of the intercalated MOF crystals, leading to individual freestanding MOF nanosheets. This chemical exfoliation process can proceed efficiently at room temperature to produce ultrathin (∼1 nm) 2D MOF nanosheets in ∼57% overall yield. The obtained ultrathin nanosheets exhibit efficient and far superior heterogeneous photocatalysis performance compared with the corresponding bulk MOF