Set-point Variations Research Articles

Steam–oxygen gasification of forest residues and bark followed by hot gas filtration and catalytic reforming of tars: Results of an extended time test

Steam–oxygen gasification in a Circulating Fluidized-bed (CFB) reactor was developed for producing transportation fuels from different wood residues. This article presents the results of a two week test campaign, in which crushed forest residues and industrial bark mixture were used as the feedstocks. The aim of the work was to carry out extended time testing of the developed gasification and hot gas cleaning process and to determine the fate of different gas contaminants and trace components of wood. In the test runs, wood fuels were gasified in the CFB reactor at a 0.2–0.25MPa pressure using a mixture of steam and oxygen as the gasification agent. A mixture of sand and dolomite was used as the bed material in order to maintain stable fluidization and to catalyse in-situ tar decomposition before hot filtration. Raw gas was filtered at ca. 550°C and the filtered gas was then led into a two-stage catalytic tar reformer. The gasifier performance and the concentrations of different gas contaminants were determined at four different operating variable set points during a total of 215h of operation. The results for carbon conversion efficiency, raw gas composition and the fate of fuel nitrogen, chlorine and trace metals are presented in this paper. The concentrations of gas contaminants were determined after the ceramic filter unit and after the catalytic reformer. The conversion efficiencies for hydrocarbon gases, tars and ammonia in the reformer are also presented. The test run was carried out as a continuous operation without any interruptions or operational problems.

Identification, control and robustness analysis of a robotic system using fractional control

This paper presents the identification, control and robustness analysis for a robotic system of 1 degree of freedom using fractional and integer PID controllers. Initially, an ADAMS-MATLAB co-simulation model is built to simulate the dynamic model of robotic system. The identification of the dynamic model parameters of the system employs the recursive least-squares method. The controller design uses a linearized model of the robotic system obtained by the method of input-output linearization, and then, the fractional PID and integer PID controllers are designed based on it. The robustness of the PID controllers is analyzed in the frequency and the time domain. The robustness analysis in the frequency domain uses The Gang of Four (sensibility, complementary sensibility, sensibility of the plant and sensibility of the controller effort). The time domain analysis uses the ADAMS-MATLAB co-simulation model, considering external disturbances, noise in the feedback loop, parametric uncertainty and set-point variation, using as performance criteria ISE, average value and standard deviation of the control action. The tests are performed for positioning and tracking tasks. The obtained results show that the fractional PID controller has a better behavior in presence of external disturbances and lower energy consumption.

Rational Basis Functions in Iterative Learning Control—With Experimental Verification on a Motion System

Iterative learning control (ILC) approaches often exhibit poor extrapolation properties with respect to exogenous signals, such as setpoint variations. This brief introduces rational basis functions in ILC. Such rational basis functions have the potential to both increase performance and enhance the extrapolation properties. The key difficulty that is associated with these rational basis functions lies in a significantly more complex optimization problem when compared with using preexisting polynomial basis functions. In this brief, a new iterative optimization algorithm is proposed that enables the use of rational basis functions in ILC for single-input single-output systems. An experimental case study confirms the advantages of rational basis functions compared with preexisting results, as well as the effectiveness of the proposed iterative algorithm.

Experimental Study on a Novel Optimal Differential Pressure Reset Method for Online Application in Chilled Water System

This paper firstly define most unfavorable thermodynamic loop to reflect the dynamic situation of terminal cooling load distribution, then present its online identification method that can easily applied for optimal reset of differential pressure. We use the optimal domain of maximum valve position among whole braches in chilled water system to calculate the variation of differential pressure setpoint, and control the pumps according to the optimal setpoint. We applied presented method to a VWF retrofit project in a Suzhou factory building and evaluate its energy saving effect through its equipped building energy management system. Experimental results showed that presented method can achieve 22%-53% pump power consumption savings compared to traditional control method. During the test period, the average level of valve position of presented method was highest among all tested control strategies. The optimal reset method of differential pressure presented in this paper is an energy efficient, high stability and easily online applied control method.

Host genetic and viral determinants of HIV-1 RNA set point among HIV-1 seroconverters from sub-saharan Africa.

We quantified the collective impact of source partner HIV-1 RNA levels, human leukocyte antigen (HLA) alleles, and innate responses through Toll-like receptor (TLR) alleles on the HIV-1 set point. Data came from HIV-1 seroconverters in African HIV-1 serodiscordant couple cohorts. Linear regression was used to determine associations with set point and R(2) to estimate variation explained by covariates. The strongest predictors of set point were HLA alleles (B*53:01, B*14:01, and B*27:03) and plasma HIV-1 levels of the transmitting partner, which explained 13% and 10% of variation in set point, respectively. HLA-A concordance between partners and TLR polymorphisms (TLR2 rs3804100 and TLR7 rs179012) also were associated with set point, explaining 6% and 5% of the variation, respectively. Overall, these factors and genital factors of the transmitter (i.e., male circumcision, bacterial vaginosis, and use of acyclovir) explained 46% of variation in set point. We found that both innate and adaptive immune responses, together with plasma HIV-1 levels of the transmitting partner, explain almost half of the variation in viral load set point. After HIV-1 infection, uncontrolled virus replication leads to a rapid increase in HIV-1 concentrations. Once host immune responses develop, however, HIV-1 levels reach a peak and subsequently decline until they reach a stable level that may persist for years. This stable HIV-1 set point represents an equilibrium between the virus and host responses and is predictive of later disease progression and transmission potential. Understanding how host and virus factors interact to determine HIV-1 set point may elucidate novel mechanisms or biological pathways for treating HIV-1 infection. We identified host and virus factors that predict HIV-1 set point in people who recently acquired HIV-1, finding that both innate and adaptive immune responses, along with factors that likely influence HIV-1 virulence and inoculum, explain ∼46% of the variation in HIV-1 set point.

Minimum variance control of organic Rankine cycle based waste heat recovery

Abstract In this paper, an online self-tuning generalized minimum variance (GMV) controller is proposed for a 100 KW waste heat recovery system with organic Rankine cycle (ORC). The ORC process model is formulated by the controlled autoregressive moving average (CARMA) model whose parameters are identified using the recursive least squares (RLS) algorithm with forgetting factor. The generalized minimum variance algorithm is applied to regulate ORC based waste heat recovery system. The contributions of this work are twofold: (1) the proposed control strategy is formulated under the data-driven framework, which does not need the precise mathematic model; (2) this proposed method is applied to handle tracking set-point variations and process disturbances by improved minimum objective GMV function. The performance of GMV controller is compared with the PID controller. The simulation results show that the proposed strategy can achieve satisfactory set-point tracking and disturbance rejection performance.

Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an on-line SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately. The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for large-magnitude set point changes and variations in process parameters.

A Performance Monitoring Tool to Quantify Valve Stiction in Control Loops

The paper presents main features of a performance monitoring system, which allows stiction quantification. It implements a methodology which permits one to reproduce the unknown stem position, without requiring any additional knowledge, based only on data normally registered in industrial plants (controller output OP, controlled variable PV and set point SP). A general procedure is proposed to discard data for which quantification is very likely to give wrong indications and to restrict the application to appropriate cases. Simulations show that several sources of perturbations can be eliminated; on the contrary, the presence of external disturbances may alter the reliability of stiction evaluation and then stiction diagnosis techniques must be applied firstly. Results are confirmed by application to industrial valves for repeated acquisitions, in the framework of a performance monitoring system implemented for continuous supervision of control loops and valve maintenance scheduling and checking.

Scalar control of an induction motor using a neural sensorless technique

The induction motor plays a key role in many industrial sectors, primarily due to its robustness and low cost. When the load coupled to the motor axis requires speed control, some of the drive and control strategies are based on the estimated axis speed of the motor. The direct measurement of this quantity reduces its robustness and increases the driver implementation cost. This paper proposes an alternative methodology for estimating the speed of a three phase induction motor driven by a voltage source inverter, using space vector modulation under the scalar control strategy and based on artificial neural networks. To validate the performance of the proposed method under motor load torque and speed reference set point variations, simulation and experimental results are presented.

Sliding Mode Control with Predictive PID Sliding Surface for Improved Performance

In this paper, a sliding mode control system with a predictive proportional-integral-derivative (PPID-SMC) sliding surface is proposed. A robust sliding mode controller is suggested to track the desired trajectory despite uncertainty, set point variations, and external disturbances. The proposed sliding mode controller is chosen to ensure the stability of overall dynamics during the reaching phase and sliding phase. The chattering problem is overcome using a hyperbolic tangent function for the sliding surface. Simulation example is given to illustrate the use of the proposed structure for better performance in terms of time domain specifications over some existing design methods.

Comparative control strategies for roller shades with respect to daylighting and energy performance

Traditionally, automated shading operation includes open-closed strategies trying to maintain a comfortable environment while controlling glare and solar gains. Four different dynamic shading control strategies with constant and variable set points were developed and studied in this paper using year-round transient integrated thermal and lighting simulation, to investigate their impact on outdoor view, daylighting metrics, thermal loads and energy consumption as well as on excessive illuminance that can cause visual discomfort in private offices. The strategies and generic and can be applied to any location, orientation and climate if appropriate set points are selected.The results showed that: (i) shades remain open for a significant portion of working hours depending on orientation and weather conditions (ii) controlling shades based on solar radiation as suggested in previous studies might not be an effective method; instead, illuminance thresholds are probably more appropriate (iii) the third control strategy leads to reduced source energy consumption and maximized daylight utilization; however, careful consideration of interior illuminances is needed to avoid the risk of glare; (iv) differences in annual source energy consumption between control strategies range from 10.1% to 34.4% depending on glazing and shading properties and (v) different strategies should be used in different orientations. Validation of results with full-scale experiments is presented for representative cases. The interactions and integration between daylighting benefits and thermal requirements need to be studied through the interplay between lighting energy use, solar and internal heat gains, while considering comfort parameters that vary depending on the shading control strategy used.

Partner Characteristics Predicting HIV-1 Set Point in Sexually Acquired HIV-1 Among African Seroconverters

Plasma HIV-1 RNA set point is an important predictor of HIV-1 disease progression. We hypothesized that inoculum size and HIV-1 exposure prior to HIV-1 transmission may modulate set point. We evaluated predictors of set point among 141 African HIV-1 seroconverters and their HIV-1-infected study partners. We compared characteristics of seroconverters and their HIV-1-infected partners and HIV-1 set point. Data were from a clinical trial of genital HSV-2 suppression with acyclovir to reduce HIV-1 transmission in HIV-1 serodiscordant couples with HIV-1 transmission linkage assigned through virus sequencing. Our analysis includes data from all transmissions including those with transmission linkage to the HIV-1-infected "source partner" and those that were not linked to their HIV-1-infected study partner. In multivariable analysis, higher plasma HIV-1 in source partners was associated with higher seroconverter set point ( + 0.44 log10 copies/ml per log(10) source partner plasma HIV-1, p < 0.001). In addition, bacterial vaginosis (BV) among female source partners near the time of infection was associated with higher set point in their male seroconverters ( + 0.49 log(10), p = 0.04). Source partner characteristics associated with lower set point included male circumcision ( - 0.63 log(10), p = 0.03) and assignment to acyclovir ( - 0.44 log10, p = 0.02). The proportion of variation in set point explained by plasma HIV-1 RNA of the source partner, after controlling for other factors, was 0.06. Source partner plasma HIV-1 level is the most significant predictor of seroconverter set point, possibly reflecting characteristics of the transmitted virus. Acyclovir use, BV among women source partners, and circumcision among male source partners may alter the set point by affecting transmitted virus inoculum in the source partners' genital compartment.

Membrane configuration optimization for a murine in vitro blood–brain barrier model

A powerful experimental tool used to study the dynamic functions of the blood–brain barrier (BBB) is an in vitro cellular based system utilizing cell culture inserts in multi-well plates. Currently, usage of divergent model configurations without explanation of selected variable set points renders data comparisons difficult and limits widespread understanding. This work presents for the first time in literature a comprehensive screening study to optimize membrane configuration, with aims to unveil influential membrane effects on the ability of cerebral endothelial cells to form a tight monolayer. First, primary murine brain endothelial cells and astrocytes were co-cultured in contact and non-contact orientations on membranes of pore diameter sizes ranging from 0.4μm to 8.0μm, and the non-contact orientation and smallest pore diameter size were shown to support a significantly tighter monolayer formation. Then, membranes made from polyethylene terephthalate (PET) and polycarbonate (PC) purchased from three different commercial sources were compared, and PET membranes purchased from two manufacturers facilitated a significantly tighter monolayer formation. Models were characterized by transendothelial electrical resistance (TEER), sodium fluorescein permeability, and immunocytochemical labeling of tight junction proteins. Finally, a murine brain endothelial cell line, bEnd.3, was grown on the different membranes, and similar results were obtained with respect to optimal membrane configuration selection. The results and methodology presented here on high throughput 24-well plate inserts can be translated to other BBB systems to advance model understanding.

TSH Measurement and Its Implications for Personalised Clinical Decision-Making

Advances in assay technology have promoted thyrotropin (TSH) measurements from participation in a multi-analyte assessment of thyroid function to a statistically defined screening parameter in its own right. While this approach has been successful in many ways, it has some grave limitations. This includes the basic question of what constitutes an agreed reference range and the fact that the population-based reference range by far exceeds the variation of the intraindividual set point. Both problems result in a potential misdiagnosis of normal and pathological thyroid function in a substantial proportion of patients. From a physiological perspective, TSH plays an integrated role in thyroid homeostasis. Few attempts have been made to adopt physiological insights into thyroid homeostasis for medical decision-making. Some emerging novel findings question the widely assumed log-linear TSH-FT4 relationship over the entire thyroid function spectrum. This data favours more complex hierarchically structured models. With a better understanding of its role in thyroid homeostasis in thyroid health and disease, TSH can be revisited in the context of thyroid regulation. This, in turn, could help overcome some of the limitations arising from its isolated statistical use and offer new prospects towards a more personalised interpretation of thyroid test results.

Optimum Setting of Controller Using Soft Computing Techniques for a Chemical System

Problem statement: The aim of this study is to present an intelligent tuning technique for PID controller that are simple and still result in good closed loop behavior. The idea is to start wit h a tuned conventional PID controller, replace it with an equivalent intelligent controllers like Fuzzy, ANN, Genetic and PSO techniques implies fine tuned nonlinear PID controller which is most suitable for nonlinear process like Continuous stirred tank reactor. The performance of various optimization techniques and intelligent techniques are compared. Approach: In this study we present soft computing techniques to design and tune the PID controller. The objective wss tominimise the steady state error and to obtain the optimum response. Results: The comparisons amoung the Conventional PID, Fuzzy Sliding PID, Simulated Anneling PID and PSO tuned PID controllers PSO PID implies better result for the nonlinear chemical process. Conclusion: With the nonlinear model of CSTR process the PSO tuned PID controller implies the op timum response for both setpoint and load variations.

Parallel evolution of thermophilia: daily and seasonal foraging patterns of heat‐adapted desert ants: Cataglyphis and Ocymyrmex species*

In the deserts of northern and southern Africa, respectively, ants of the genera Cataglyphisfoerster (Formicinae) and Ocymyrmexemery (Myrmicinae) occupy the same ecological niche, which comprises that of a strictly diurnal thermophilic scavenger. Their daily foraging activities exhibit a bimodal pattern in summer and unimodality or complete inactivity in winter. The present study investigates whether these overall patterns are a result of endogenous annual activity rhythms of the colony or are triggered directly by the prevailing ambient temperatures. By exploiting various seasonal temperature regimes and, in particular, by creating near‐nest winter conditions experimentally in summer, it is shown that the latter hypothesis is generally true. However, there are daily and annual variations in the temperature set points at which foraging activities start and finish. These temperatures are lower in the winter than in the summer months and, in summer, they are lower in the morning than in the afternoon. The level of foraging activity in the afternoon reaches maximum values at surface temperatures of 60–63 °C. This means that, in summer months, these thermophilic ants concentrate their foraging activities into a period of almost lethal temperature regimes, during which they have to devote a substantial portion of their time outside the nest to respite (i.e. cooling‐off) behaviour.

Supervisory control based on closed‐loop adaptive control approach of nonlinear systems: application to CSTR process

AbstractIn the last few years there has been a great deal of interest in the application of adaptive and supervisory control of linear systems using multi‐models and multi‐controllers based on switching and tuning algorithms. In this paper a supervisory control methodology based on a closed‐loop adaptive control approach is developed for nonlinear systems and evaluated in a simulation of a nonlinear continuous stirred tank reactor (CSTR) process for two scenarios according to different set‐point variations. The simulation results show the effectiveness in terms of performance and robustness characteristics of the proposed method.Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

Knowledge-based system for automatic MBR control

MBR technology is currently challenging traditional wastewater treatment systems and is increasingly selected for WWTP upgrading. MBR systems typically are constructed on a smaller footprint, and provide superior treated water quality. However, the main drawback of MBR technology is that the permeability of membranes declines during filtration due to membrane fouling, which for a large part causes the high aeration requirements of an MBR to counteract this fouling phenomenon. Due to the complex and still unknown mechanisms of membrane fouling it is neither possible to describe clearly its development by means of a deterministic model, nor to control it with a purely mathematical law. Consequently the majority of MBR applications are controlled in an "open-loop" way i.e. with predefined and fixed air scour and filtration/relaxation or backwashing cycles, and scheduled inline or offline chemical cleaning as a preventive measure, without taking into account the real needs of membrane cleaning based on its filtration performance. However, existing theoretical and empirical knowledge about potential cause-effect relations between a number of factors (influent characteristics, biomass characteristics and operational conditions) and MBR operation can be used to build a knowledge-based decision support system (KB-DSS) for the automatic control of MBRs. This KB-DSS contains a knowledge-based control module, which, based on real time comparison of the current permeability trend with "reference trends", aims at optimizing the operation and energy costs and decreasing fouling rates. In practice the automatic control system proposed regulates the set points of the key operational variables controlled in MBR systems (permeate flux, relaxation and backwash times, backwash flows and times, aeration flow rates, chemical cleaning frequency, waste sludge flow rate and recycle flow rates) and identifies its optimal value. This paper describes the concepts and the 3-level architecture of the knowledge-based DSS and details the knowledge-based control module. Preliminary results of the application of the control module to regulate the air flow rate of an MBR working with variable flux demonstrates the usefulness of this approach.

Additive Contribution of HLA Class I Alleles in the Immune Control of HIV-1 Infection

Previous studies have identified a central role for HLA-B alleles in influencing control of HIV infection. An alternative possibility is that a small number of HLA-B alleles may have a very strong impact on HIV disease outcome, dominating the contribution of other HLA alleles. Here, we find that even following the exclusion of subjects expressing any of the HLA-B class I alleles (B*57, B*58, and B*18) identified to have the strongest influence on control, the dominant impact of HLA-B alleles on virus set point and absolute CD4 count variation remains significant. However, we also find that the influence of HLA on HIV control in this C-clade-infected cohort from South Africa extends beyond HLA-B as HLA-Cw type remains a significant predictor of virus and CD4 count following exclusion of the strongest HLA-B associations. Furthermore, there is evidence of interdependent protective effects of the HLA-Cw*0401-B*8101, HLA-Cw*1203-B*3910, and HLA-A*7401-B*5703 haplotypes that cannot be explained solely by linkage to a protective HLA-B allele. Analysis of individuals expressing both protective and detrimental alleles shows that even the strongest HLA alleles appear to have an additive rather than dominant effect on HIV control at the individual level. Finally, weak but significant frequency-dependent effects in this cohort can be detected only by looking at an individual's combined HLA allele frequencies. Taken together, these data suggest that although individual HLA alleles, particularly HLA-B, can have a strong impact, HIV control overall is likely to be influenced by the additive effect of some or all of the other HLA alleles present.

Optimal supervisory control of a central chilled water plant with heuristic search sequential quadratic programming

A new methodology for adapting rigorous simulation programs to optimal supervisory control of a central chilled water plant is proposed in this article, which solves plant operation mode optimization and set points optimization by combining heuristic search with sequential quadratic programming. The mathematical basis of this algorithm is developed. A new derivative calculation strategy is introduced in set points optimization. This approach is applied to a central chilled water plant which consists of three chillers, two 3-cell cooling towers, three chilled water pumps and three condenser water pumps. Model verification study is performed. The optimal sequence of operation, set points of the decision variables at given load demand and weather condition are calculated. The plant performance and optimal control results are discussed.