Investigation on the Simulation of Control Strategy for a SCR System

In order to fulfill future emission standards for middle and heavy-duty vehicles like state Ⅳ and Ⅴ, advanced measures on exhaust gas and engine functionality are required. Selective Catalytic Reduction (SCR) technology is the unique technology currently which can improve the emission and reduce fuel consumption simultaneously. Firstly the reductants and its chemical reactions, SCR system configurations and its working principle and urea dosing control strategy are introduced. Then tests are conducted on a diesel engine with SCR system at bench. The results of ESC cycle show that NOx emission is decreased by more than 67% with the open-loop control strategy. Additionally, the urea and fuel consumption and ammonia leakage have been compared and analyzed respectively, the experiment data indicates that the urea water solution consumption ratio is only 5.7% of fuel for this SCR system, while its average ammonia slip is below 5 ppm.

Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination ( R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.

Urea-based selected catalytic reduction (SCR) systems are effective ways in diesel engine after-treatment systems to meet increasingly stringent emission regulations. To achieve high NOx reduction efficiency and low NH3 slip, the control of the SCR system becomes more challenging, especially in transient operating conditions with model uncertainties. To effectively address this issue, this paper proposed a compound control strategy with a switching mechanism between an active disturbance rejection (ADR) controller and a zero-input controller. The ADR controller estimates and rejects the total (internal and external) disturbances from the SCR system when the exhaust gas temperature is high and its variation is small. The zero-input controller is used to lower ammonia surface coverage ratio to avoid high ammonia slip when exhaust gas temperature suddenly rises. The proposed control strategy is validated through a high-fidelity GT-Power simulation for a light-duty diesel engine over steady states and federal test procedure (FTP-75) test cycle. Its effectiveness is demonstrated especially in rapidly transient conditions with model uncertainties.

Ammonia is one of the most common reductants for the automotive selective catalytic reduction (SCR) system owing to its high NO2 reduction (deNOx) efficiency. However, ammonia carriers for the SCR system have sluggishly evolved to achieve rapid ammonia dosing. In this study, the MOFs [M2(adc)2(dabco)] (M = Co, Ni, Cu, Zn) were synthesized and characterized as ammonia carriers. Among the four obtained MOFs, Ni2(adc)2(dabco) possessed the highest surface area, 772 m2/g, highest ammonia uptake capacity, 12.1 mmol/g, and stable cyclic adsorption-desorption performance. All the obtained MOFs demonstrated physisorption of ammonia and rapid kinetics of ammonia adsorption and desorption. Compared with halide ammonia carrier MgCl2, the obtained MOFs showed four times faster adsorption kinetics to reach 90% of the ammonia uptake capacity. For the ammonia desorption, the Ni2(adc)2(dabco) provided 6 mmol/g ammonia dosing when temperature reached 125 °C in the first 10 min, which was six times of the ammonia dosing from Mg(NH3)6Cl2. The results offer a solution to shorten the buffering time for ammonia dosing in the SCR system.

Presented paper describes monitoring of basic parameters for selective catalytic reduction (SCR) system used in an agricultural tractor. SCR systems are used to reduce emissions of nitrogen oxides (NOx) produced by combustion of fuel. The usage of SCR catalytic converters entails certain disadvantages in the use of reducing agent and the necessity of suitable operating conditions to achieve optimum efficiency of the catalytic converter. This paper aims to predict consumption of AdBlue depending on the temperature of SCR catalytic converter, which reflects the engine load and monitoring the effectiveness of SCR catalytic converter when operating a tractor engine with a maximum dose of fuel. To fulfill those aims, the measurements have been performed on the Case Puma 185 CVX agricultural tractor. As the measurement results indicate, the lowest NOx emissions correspond to high consumption of AdBlue. Other studies imply that the catalytic converter operates at optimal operating temperature and with the highest efficiency of NOx emission reduction. The effectiveness of NOx emission reduction is thus affected not only by quantity of injected reagent but also by catalytic converter thermal load. Further measurement results indicate that the lowest amount of emissions of NOx (and the highest efficiency rate) is achieved by catalytic converter in a range in which the engine operates with the highest engine efficiency.

Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.

Normal practice in continuous digester operation is to set the production rate through the chip meter speed. This speed is seldom, if ever, adjusted except to change production, and most of the other digester inputs are ratioed to it. The inherent assumption is that constant chip meter speed equates to constant dry mass flow of chips. This is seldom, if ever, true. As a result, the actual production rate, effective alkali (EA)-to-wood and liquor-to-wood ratios may vary substantially from assumed values. This increases process variability and decreases profits. In this report, a new continuous digester production rate control strategy is developed that addresses this shortcoming. A new noncontacting near infrared–based chip moisture sensor is combined with the existing weightometer signal to estimate the actual dry chip mass feedrate entering the digester. The estimated feedrate is then used to implement a novel feedback control strategy that adjusts the chip meter speed to maintain the dry chip feedrate at the target value. The report details the results of applying the new measurements and control strategy to a dual vessel continuous digester.

Abstract Objective: Evidence-based infection control strategies are needed for healthcare workers (HCWs) following high-risk exposure to severe acute respiratory coronavirus virus 2 (SARS-CoV-2). In this study, we evaluated the negative predictive value (NPV) of a home-based 7-day infection control strategy. Methods: HCWs advised by their infection control or occupational health officer to self-isolate due to a high-risk SARS-CoV-2 exposure were enrolled between May and October 2020. The strategy consisted of symptom-triggered nasopharyngeal SARS-CoV-2 RNA testing from day 0 to day 7 after exposure and standardized home-based nasopharyngeal swab and saliva testing on day 7. The NPV of this strategy was calculated for (1) clinical coronavirus disease 2019 (COVID-19) diagnosis from day 8–14 after exposure, and for (2) asymptomatic SARS-CoV-2 detected by standardized nasopharyngeal swab and saliva specimens collected at days 9, 10, and 14 after exposure. Interim results are reported in the context of a second wave threatening this essential workforce. Results: Among 30 HCWs enrolled, the mean age was 31 years (SD, ±9), and 24 (80%) were female. Moreover, 3 were diagnosed with COVID-19 by day 14 after exposure (secondary attack rate, 10.0%), and all cases were detected using the 7-day infection control strategy: the NPV for subsequent clinical COVID-19 or asymptomatic SARS-CoV-2 detection by day 14 was 100.0% (95% CI, 93.1%–100.0%). Conclusions: Among HCWs with high-risk exposure to SARS-CoV-2, a home-based 7-day infection control strategy may have a high NPV for subsequent COVID-19 and asymptomatic SARS-CoV-2 detection. Ongoing data collection and data sharing are needed to improve the precision of the estimated NPV, and here we report interim results to inform infection control strategies in light of a second wave threatening this essential workforce.

Benchmark problems have been used to evaluate the performance of a variety of robust control design methodologies by many control engineers over the past 2 decades. A benchmark is a simple but meaningful problem to highlight the advantages and disadvantages of different control strategies. This paper verifies the performance of a new control strategy, which is called combined feedforward and feedback control with shaped input (CFFS), through a benchmark problem applied to a two-mass-spring system. CFFS, which consists of feedback and feedforward controllers and shaped input, can achieve high performance with a simple controller design. This control strategy has several unique characteristics. First, the shaped input is designed to extract energy from the flexible modes, which means that a simpler feedback control design based on a rigid-body model can be used. In addition, only a single frequency must be attenuated to reduce residual vibration of both masses. Second, only the dynamics between control force and the first mass need to be considered in designing both feedback and feedforward controllers. The proposed control strategy is applied to a benchmark problem and its performance is compared with that obtained using two alternative control strategies.

In this work, an optimal state feedback control strategy is proposed for non-linear, distributed-parameter processes. For different values of a given parameter susceptible to upsets, the strategy involves off-line computation of a repository of optimal open-loop states and gains needed for the feedback adjustment of control. A gain is determined by minimizing the perturbation of the objective functional about the new optimal state and control corresponding to a process upset. When an upset is encountered in a running process, the repository is utilized to obtain the control adjustment required to steer the process to the new optimal state. The strategy is successfully applied to a highly non-linear, gas-based heavy oil recovery process controlled by the gas temperature with the state depending non-linearly on time and two spatial directions inside a moving boundary, and subject to pressure upsets. The results demonstrate that when the process has a pressure upset, the proposed strategy is able to determine control adjustments with negligible time delays and to navigate the process to the new optimal state.