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分工情况:•xxx :•负责主讲•xxx :•负责主持•xxx :•负责ppt制作•xxx :•负责ppt制作•xxx :•负责收集资料,参与ppt制作背景:燃料燃烧就会向大气中释放二氧化碳（CO2）,而CO2的聚集会导致全球变暖和海水的酸化。
碳的捕集和储存（CCS）技术可以在源头捕集CO2并将其封储在存储设备中，从而减少温室效应。
背景:电力生产中所排放的CO2所占比例最大。
大约60%的排放来自于大型的固定排放源（10万吨CO2/年），使用天然气或煤作为燃料的火电厂大约占其中的30%。
全球大约有2000座年排放量超过100万吨CO2的电站，它们是CO2捕集的最重要的潜在目标。
碳捕集CO2是一种不稳定的气体，要从惰性气体（主要是氮气）中捕集CO2是非常困难的，成本相当高，还需要额外的能量。
由于传统的火电站是在大气压力下燃烧天然气或煤炭，在清洁的惰性气体排放到大气以前，CO2必须在非常困难的条件下分离出来。
这样做的目的是得到浓缩的易于传输的高压CO2气流。
碳捕集的三种方法之预燃烧CO2在燃烧之前分离出来，这一过程比较复杂但潜力巨大。
碳捕集的三种方法之后燃烧CO2从燃烧后的废气中分离出来。
这种方法将电站的能量输出减少了20%以上，但是CCS技术的开发重点目前集中在这一领域。
因为这种方法已经在用，而且现有的电站可以进行更新。
碳捕集的三种方法之全氧燃烧工艺用氧气替代燃烧过程中的空气，因此废气中主要含有CO2和水。
CO2可以通过冷凝分离出来。
现有电站可以根据全氧燃烧工艺的要求进行翻新，这一工艺颇具前景，目前一些示范电站已在建设之中。
碳捕集难题传统火电站的废气率非常高，例如一座400 MW的天然气发电设备废气率超过200万立方米/小时。
另外，高浓缩的溶剂输送到再生塔后释放出CO2，然后稀释的溶剂被输送回吸收塔中。
如何使液体分布器在塔柱交叉部位均匀喷洒是一大难题。
解决方案苏尔寿的液体分布器测试装置可以解决这些难题。
An intelligent system for monitoring and diagnosis of the CO 2capture processQing Zhou a ,Christine W.Chan a ,⇑,Paitoon Tontiwachwuthikul ba Energy Informatics Laboratory,Faculty of Engineering,University of Regina,Regina,Saskatchewan,Canada S4S 0A2bProcess Systems Engineering Laboratory,International Test Centre for CO 2Capture (ITC),University of Regina,Regina,Saskatchewan,Canada S4S 0A2a r t i c l ei n f o Keywords:CO 2capture DeltaV Simulate Intelligent systema b s t r a c tAmine-based carbon dioxide capture has been widely considered as a feasible ideal technology for reduc-ing large-scale CO 2emissions and mitigating global warming.The operation of amine-based CO 2capture is a complicated task,which involves monitoring over 100process parameters and careful manipulation of numerous valves and pumps.The current research in the field of CO 2capture has emphasized the need for improving CO 2capture efficiency and enhancing plant performance.In the present study,artificial intelligence techniques were applied for developing a knowledge-based expert system that aims at effec-tively monitoring and controlling the CO 2capture process and thereby enhancing CO 2capture efficiency.In developing the system,the inferential modeling technique (IMT)was applied to analyze the domain knowledge and problem-solving techniques,and a knowledge base was developed on DeltaV Simulate.The expert system helps to enhance CO 2capture system performance and efficiency by reducing the time required for diagnosis and problem solving if abnormal conditions occur.The expert system can be used as a decision-support tool that helps inexperienced operators control the plant;it can be used also for training novice operators.Ó2010Elsevier Ltd.All rights reserved.1.IntroductionThe emission of large amounts of carbon dioxide (CO 2)has caused increasing public concern regarding environmental pollu-tion and global warming.To mitigate this serious environmental problem,the CO 2capture technology has become widely accepted as useful technology for reducing CO 2emissions from industrial sources.The goal of CO 2capture is to capture and remove CO 2from industrial gas streams before it is released into the atmosphere.The amine-based CO 2capture process has become a common method for CO 2removal because it is energy efficient (Sholeh,Svendsen,Karl,&Olav,2007).In the amine-based CO 2capture pro-cess,an amine solvent is used to absorb CO 2from the flue gas,and CO 2is subsequently extracted from the amine solvent,which can then be regenerated and reused.Operation of an amine-based CO 2capture system is a complicated task because it involves mon-itoring and manipulation of 16components and a number of valves/pumps.The 16components are associated with over a 100parameters,including temperatures,flow rates,pressures,and levels of reaction instruments.The monitoring and control of crit-ical parameters is an important task in operation of the CO 2cap-ture process because it directly impacts plant performance and capture efficiency of CO 2.Since the monitoring and control task is complex,it is desirable to build a knowledge-based system that can automatically monitor,control,and diagnose the CO 2capture process.In this paper,we present research conducted with the objective of building a knowledge-based expert system that can monitor,control,and diagnose the CO 2capture processes at the International Test Centre for CO 2Capture (ITC)located at the University of Regina in Saskatchewan,Canada.The system is called the Knowledge-Based System for Carbon Dioxide Capture (KBSCDC).The knowledge base consists of domain knowledge about:(1)the plant components and their attributes,and (2)the important process parameters and their desired operat-ing ranges.The knowledge base also consists of the remedial actions that would address these abnormal situations.The KBSCDC system can help the operator monitor the operating conditions of the CO 2capture pilot plant by continuously comparing the mea-sured values from sensors with normal or desired values.Plant components that have abnormal parameter values indicate that abnormal operating conditions have occurred.Deviations from the normal ranges would set off an alarm to advise the operator that a problem has occurred.The KBSCDC can conduct real-time monitoring and diagnosis,as well as suggest remedies for any abnormality detected,thereby improving the performance effi-ciency of the plant.An initial prototype of the system was developed on G2(trade-mark of Gensym Corporation,USA),which is an object-oriented expert system development tool.However,the prototype can only monitor reaction instruments and diagnose their abnormalities.The system did not include the process control strategies applied0957-4174/$-see front matter Ó2010Elsevier Ltd.All rights reserved.doi:10.1016/j.eswa.2010.12.010⇑Corresponding author.Tel.:+13065855225;fax:+13065854855.E-mail address:christine.chan@uregina.ca (C.W.Chan).to the control devices.Details of the prototype system have been presented in Zhou,Chan,and Tontiwachiwuthikul(2009).This pa-per presents an improved version of the knowledge-based expert system that was implemented with DeltaV Simulate(trademark of Emerson Corp.,USA).DeltaV Simulate provides control utilities which enables the configuration of control strategies in small mod-ular components.These modules link algorithm,conditions,and provide control over thefield devices such as pumps and valves. Modules can communicate directly with each other,and can be coordinated by other modules to perform higher-level control strategies.The modules deploy different algorithms such as sequential function chart(SFC)and function block diagram(FBD). SFC is made up of a series of steps,transitions,and actions and it is used for representing the sequence of controlling strategies which contain multiple states.FBD is made up of interconnected function blocks,which process the incoming signals and send the signal to the control devices.Each function block contains standard process control algorithm and parameters that customize the algo-rithm to perform a particular function in the process control. Therefore,the new version of KBSCDC has the following additional functions compared to the earlier G2version:(1)modules for dif-ferent control devices are configured based on their characteristics, and(2)the control strategies applied to the control devices are simulated.The paper discusses design and development of the improved version of the knowledge-based system,and demonstrates use of the system by using problems scenarios that occur due to abnor-mal conditions.The paper is organized as follows:Section2pre-sents the background literature relevant to the area of CO2 capture and the knowledge acquisition approach of the inferential modeling technique adopted in this research.Section3describes the process of development of the knowledge base.Section4pre-sents design and implementation of the system on DeltaV Simu-late based on the developed knowledge base.Application of the system is demonstrated using a case study in Section5.Section 6gives a conclusion and includes some discussion about future work.2.Background literature2.1.Studies of amine-based CO2captureThe study of amine-based CO2capture has been ongoing for the last decade.The general objective of the study is to improve effec-tiveness and efficiency of the CO2capture process.The research has been primarily conducted in the following two areas:(1)Study of the behaviour of the conventional amine solventsand development of new or improved solvents with higher CO2absorption capacities,faster CO2reaction rates, higher degradation resistance,and lower heat consump-tion for regeneration.The studies of corrosion were con-ducted in CO2absorption systems using different types of aqueous amine solvents including methyldiethanol-amine(MDEA),diethanolamine(DEA),2-amino-2-methyl-1-propanol(AMP)and monoethanolamine(MEA),and it was found that the corrosiveness increased in the order of MDEA<DEA<AMP<MEA(Veawab,Tontiwachwuthikul, &Bhole,1997;Veawab,Tontiwachwuthikul,&Chakma, 1999).It was suggested that2-(2-aminoethyl-amino)eth-anol(AEEA)is a potentially good absorbent for capturing CO2because of its high absorption rate and high absorp-tion capacity(Sholeh et al.,2007).Chakma(1997)pro-posed that utilization of mixed solvents could reduce energy consumption and solve a number of operationalproblems.Idem et al.(2006)evaluated the benefits of using mixed MEA/MDEA solvent for CO2capture and found that a very large heat-duty reduction could be achieved by using a mixed MEA/MDEA solvent instead ofa single MEA solvent.(2)Selection of appropriate solvents for different applications toreduce the energy penalty.It was proposed that the crucial criteria of solvent selection include feed gas characteristics such as composition,pressure,temperature,and the treated gas specifications(Veawab,Aroonwilas,Chakma,& Tontiwachwuthikul,2001).White,Strazisar,Granite,and Hoffman(2003)suggested that solvent selection is influ-enced by solvent characteristics such as CO2absorption capability and rates and operational issues of the process such as corrosion potential and solvent stability.These fac-tors influence the equipment size,solvent consumption and heat consumption.Tontiwachwuthikul(1996)proposed that the best solvents can be formulated by blending differ-ent amines to take full advantage of the desirable properties of each solvent.Some observations that can be derived from our survey of past research conducted in thefield of CO2capture include the following:(1)With respect to the objective of improving efficiency of theCO2capture process,previous research studies rarely focused on using automation for supporting the process of monitoring and control of the CO2capture system as a means for optimizing the plant performance and enhancing efficiency of the CO2capture process.Operation of a CO2cap-ture system is a complicated task because it involves control of over100parameters.If these parameters are monitored and controlled effectively,the entire plant can work under desirable conditions and efficiency of the CO2capture pro-cess can be greatly enhanced.(2)Application of artificial intelligence technologies has notbeen made to the CO2capture domain.Since operation ofa CO2capture system is extremely complicated,the processoperators have accumulated significant knowledge and problem-solving skills over time.This experience is exclu-sive and hard to develop,and it is desirable to capture and encode the human expertise into a knowledge-based system for documentation and training purposes.Therefore,the objective of this study is to develop a knowledge-based system for monitoring and control of the CO2capture pro-cess.Such a research study would helpfill the gap in research for thefield of CO2capture process.2.2.Inferential modeling techniqueAn important prerequisite for developing a knowledge-based system is to acquire expertise that can be encoded in the knowl-edge base.For acquiring knowledge on the CO2capture process, we adopted the inferential modeling technique,which is derived from the inferential model.An inferential model is a generic cate-gorization of knowledge types.It functions as a‘‘conceptual map’’to aid the knowledge engineer to identify and classify elements of the elicited expertise(Chan,Tontiwachwuthikul,&Cercone,1995). Based on this map,the inferential modeling technique or IMT supports‘‘an iterative-refinement of knowledge elements in a problem-domain that provides top-down guidance on the knowl-edge types required for problem solving’’(Chan,Peng,&Chen, 2002).The resulting inferential model consists of the following four levels of knowledge:7936Q.Zhou et al./Expert Systems with Applications38(2011)7935–7946(1)Domain knowledge consists of objects,attributes,values,and relations.The objects include a set of concrete domain objects.The attributes describe the properties of the objects, which can be defined as a set of functions that receive input values and return output values.The relations describe the relationships among the objects or the attributes.(2)Inference knowledge consists of abstract objects.Theseinference level objects can be described with inference rela-tions and strength of inferences.The inference relations identify different types of relations among sets of abstract objects;the strength of the inference is associated with each inference relation and represents the relative inferential sig-nificance of the relation.(3)Task knowledge consists of a set of procedures or behaviourswhich are performed to complete a goal.A task is accom-plished by means of a method that invokes the domain and inference objects or relations involved in this task.One task can be decomposed into a number of subtasks,and the objective of this task is accomplished by coordinating all the sub-goals.(4)Strategy knowledge is defined as the knowledge used duringthe diagnostic process to decide what is the most opportune choice to make or,alternatively,to judge if it is worth exe-cuting a certain action with respect to other possible actions (Mussi,1993).The IMT was applied and the templates of domain knowledge and task knowledge were used in the process of knowledge base development for the domain of amine-based CO2capture process.2.3.Amine-based CO2capture processThe goal of CO2capture is to separate CO2from industrial gas streams before they are released into the atmosphere.The process of amine-based CO2capture at the International Test Centre for CO2capture(ITC)can be briefly described as follows:prior to CO2capture,theflue gas is cooled down and particulates and other impurities such as SO x and NO x are removed as much as possible. The pre-treatedflue gas is injected into the absorber column from the bottom,and it contacts solvent that is free of CO2or lean amine solvent,which is injected from the top of the absorber column.The amine selectively absorbs CO2from theflue gas.The amine solvent carrying CO2,which is called CO2-rich or rich amine,enters the stripper column,where the CO2is extracted from the amine sol-vent and the lean amine solvent is regenerated.The lean amine sol-vent is returned to the absorber column and used in the CO2 removal process again.The CO2stream produced is dried and post-treated,and it can be either developed to a food grade quality or pressurized and transported to a suitable site for geological stor-age.The CO2capture process is depicted in Fig.1.3.Development of a knowledge baseThe knowledge base in this study was developed in three phases:knowledge acquisition,knowledge analysis,and knowl-edge representation.In the process of knowledge acquisition,the first author acted as the knowledge engineer and interacted with the domain expert,who is the chief engineer of ITC,to acquire knowledge about problem-solving in the domain.The process of knowledge acquisition lasted1year,from January to December 2005.During the phases of knowledge analysis and representation, the knowledge engineer analyzed the verbal information collected from the expert and configured them into a conceptual model.The IMT was applied in knowledge analysis,and the knowledge was formalized into an inferential model.The IMT decomposed knowl-edge into the two levels of domain knowledge and task knowledge.3.1.Domain knowledgeDomain knowledge includes three components:the objects, their attributes,and values related to the attributes.The objects in the plant can be classified into two categories: static and dynamic.The static objects include the constructive components of the plant,which can be divided into the three classes of reaction instruments,valves,and pumps.The dynamic objects include the substances that circulate and react in the plant, i.e.,the water,amine solvent,and gases.The classification of ob-jects is shown in Fig.2,and the details are described in the follow-ing sections.3.1.1.Static objects and their attributesThe three kinds of static objects including reaction instruments, valves and pumps are discussed in details as follows.3.1.1.1.Reaction instruments.There are16primary reaction instru-ments involved in the plant.They are grouped into three main clas-ses based on their functions and listed as follows:(1)Pre-treatment section,which includes the steam boiler,micro turbine,inlet-gas scrubber.(2)Absorption-based CO2section,which includes the absorber,off-gas scrubber,lean amine storage tank,lean amine cooler,rich amine vessel,lean/rich amine exchanger,strip-per,reboiler,and reclaimer.(3)Post-conditioning section for product purification,whichincludes the reflux condenser,reflux accumulator,CO2wash scrubber,and CO2dryer unit.The attributes of the reaction instruments include the tempera-ture,pressure,or level of the instruments and the attributes of their output dynamic objects.The details of the attributes of the reaction instruments will be discussed in Section3.1.2.3.1.1.2.Valves and pumps.The valves and pumps are manipulated to control the process parameters.Therefore,all the pumps and valves are associated with the attributes of the reaction instru-ment.In terms of representation in the knowledge hierarchy,the reaction instruments’attributes are represented one level below the reaction instrument objects.Corresponding to this representa-tion,the pumps and valves are defined as modules in the system design because the modules are one level lower than the plant area in the DeltaV representational hierarchy.Valves:The valves can be categorized into two types based on their control mechanism:PID(proportional-integral-derivative) control valves and solenoid valves.While all the solenoid valves are used for controlling waterflow,the PID valves can be subdi-vided into four groups based on the substances they manipulate: (1)steam supply control valve,(2)amine control valve,(3)water control valve,and(4)gas control valve.All the PID control valves in the plant can be identified byfive attributes:the three system attributes of:(1)tag number(the label for a valve/pump),(2)name(the brief description),(3)type(the mechanism of a valve/pump),and two design attributes of(4)loca-tion(where the valve/pump is installed in the plant),and(5)distri-butionflow(the dynamic object which a valve/pump controls).The solenoid valves can be identified by the additional attribute of sta-tus,which describes their ON/OFF state under normal conditions. Also,the attribute of distributionflow determines the process parameter controlled by a valve,and the attribute of locationQ.Zhou et al./Expert Systems with Applications38(2011)7935–79467937determines the plant area to which a valve belongs in the phase of system design.Pumps:Pumps include liquid distribution pumps and gas blower pumps.The liquid distribution pumps can be divided into three subclasses according to the types of flow they control:(1)water control pumps,(2)amine control pumps,and (3)chemical flow control pumps.Like solenoid valves,all the PID control valves in the plant can be identified by six attributes including tag num-ber,name,type,location,distribution flow,and status.A sample valve and a sample pump are given in Table 1. 3.1.2.Dynamic objects and their attributesThe dynamic objects include amine solvent,water,and gas.The amine solvent can be classified into lean amine and rich amine based on the amount of CO 2it carries.The gases include flue gas (with CO 2),off gas (free of CO 2),CO 2,and steam.All the dynamic objects can be specified by the three attributes of temperature,pressure,and flow rate.Since the dynamic objects circulate and react through the entire process,the values of their attributes are constantly changing.Therefore,a decision was made to identify the properties of dynamic objects at anyparticularFig.1.Amine-based CO 2capture process flow diagram.CO 2 Capture PlantStatic ObjectsWaterDynamic ObjectsReaction InstrumentsGasesPumps ValvesSolventFlue Gas Off Gas CO 2SteamRich AmineLean AmineFig.2.Objects in CO 2capture plant.location with the tag of the sensor.Moreover,the knowledge engi-neer classified the attributes of the dynamic components with the attributes of the reaction instrument from which theyflow.For example,the attributes of the lean amine storage tank include the level and temperature of the tank itself,as well as the attri-butes of its output lean amine.They include:(1)amine storage tank level(DPT-600),(2)lean amine storage tank temperature (TE-640),(3)lean amine to absorberflow rate(FT-600),and(4) lean amine to absorber temperature(TE-600).The attributes of dynamic objects are organized in this way due to three reasons.Firstly,the performance of the reaction instru-ment directly influences the attributes of its output dynamic ob-jects;hence,it is logical to group the reaction instrument with the output dynamic objects.Secondly,this approach enables straightforward examination of the attributes of dynamic objects at different phases in the process.Thirdly,this organization simpli-fies the grouping of attributes and facilitates design and construc-tion of the KBSCDC.In this way,over100process parameters in the plant can be grouped into16reaction instrument groupings. Therefore,the entire system can be viewed in terms of16reaction components,their relevant attributes or process parameters,and the relevant valves/pumps.The values of the process parameters are monitored by the system so that if any abnormal value is de-tected,the relevant pump/valves will be manipulated to remedy the abnormal conditions.3.2.Task knowledgeThe objective of the KBSCDC is to maintain normal plant perfor-mance so that it produces CO2at the desired rate.Therefore,the main task of the system is to monitor all the reaction instruments and ensure they operate under desirable conditions.The instru-ments that have abnormal parameter values indicate that abnor-mal operating conditions have occurred.The system can monitor the operating conditions of the CO2capture plant by constantly comparing the measured values with desired parameter values. Deviation from the normal ranges of values triggers an alarm to ad-vise the operator that a problem has occurred.The system then diagnoses the abnormal state and suggests the remedial control ac-tions that would address the abnormal situation.The task of monitoring each reaction instrument includes the subtasks of monitoring its related attributes,i.e.,process parame-ters.Therefore,it is important to obtain the desirable operating ranges of the process parameters.The knowledge engineer identi-fied25critical process parameters and their normal operating ranges with the help of the domain expert;two sample parameters of the inlet-gas scrubber and their normal ranges of values are shown in Table2.Therefore,monitoring of the inlet-gas scrubber consists of the four subtasks of:(1)controlling theflow rate offlue gas into absor-ber(FT-200),(2)controlling the temperature offlue gas into absor-ber(TE-201),(3)controlling the wash waterflow rate of scrubber (FT-420),and(4)controlling the inlet-gas scrubber water level (LC-410).The two sample subtasks of controlling the water level (LC-410)and controlling the wash waterflow rate(FT-420)are dis-cussed here.They are controlled so that the parameter values fall within the values specified in Table2.If the values should fall out-side the normal ranges,the diagnosis and remedial control actions are determined by various conditions.The details of diagnosis and control actions for the sample parameter of wash waterflow rate (FT-420)are given in Table3.If the wash waterflow rate(FT-420)of the inlet-gas scrubber is less than5.0kg/m,a warning is given to the operator.The diagnosis of the situation is that the over lowflow rate of wash water could be caused by the closed water circulation pump P-420.Therefore,the remedial control action is to open pump P-420to restart water circulation between the water tank and the inlet-gas scrubber.However,if P-420is already open, then the PID valve FCV-420should be opened to increase water flow.4.System design and implementation4.1.System designThe intelligent system of KBSCDC was implemented on DeltaV Simulate(a trademark of Emerson Corp.,USA).DeltaV Simulate logically decomposes the entire system into plant areas and control modules.It supports various algorithms for implementing process control logic,and it allows the simulation of dynamic processes and real-time monitoring.The implementation of KBSCDC on DeltaV involves a hierarchy of 5levels:plant area(level1),module(level2),algorithm(level3), function block(level4),and parameter(level5).The hierarchy is shown in Fig.3.The plant areas are logical divisions of the process control sys-tem,which can be based on physical plant locations or main pro-cess functions.A plant area consists of modules,and each module is a logic control entity responsible for configuring the con-trol strategies.It contains algorithms,alarms,and other character-istics that define the process control.Algorithms define the logic steps that describe how the module behaves and how the tasks are accomplished.In this intelligent system,the function block dia-grams(FBD)were used to continuously execute control strategies. The basic component of a FBD is a function block,which contains the control algorithm and defines the behaviour of the module. Each function block contains parameters which are the user-defined data manipulated by the module’s algorithm in its calcula-tions and logic.Thefive-level hierarchy of the KBSCDC system supports a top-down approach for encoding knowledge into DeltaV Simulate. System construction on DeltaV Simulate can be explained by describing sample components of each level as shown in Fig.4, and the details are described below.Three sample plant areas include the stripper,inlet-gas scrub-ber,and absorber.In this discussion,the inlet-gas scrubber is used as an example to illustrate how a plant area is constructed.TheTable1Samples of valve/pump.Tag Name Type Distributeflow Location StatusFCV-600(valve)Lean amine to absorber control valve PID control valve Amine solvent Between absorber and lean amine storage tank N/A B-200(pump)Inletflue gas blower Gas blower Flue gas Betweenflue gas scrubber and absorber ONTable2Sample parameters and their normal operating ranges.Tag Parameter Unit Limit ValueFT-420Off gas scrubber wash waterflow rate kg/min High37.0Low 5.0LC-410Off gas scrubber water level control%High65.0Low 5.0Q.Zhou et al./Expert Systems with Applications38(2011)7935–79467939plant area of inlet-gas scrubber contains eight modules.Four of these are PID valve control modules for the process parameters of:(1)flue gasflow rate into absorber(FC-200),(2)inlet-gas scrub-ber water level control(LC-410),(3)temperature offlue gas to ab-sorber(TC-201),and(4)wash waterflow rate of inlet-gas scrubber (FC-420).The other four are2-state control modules for the pumps and solenoid valves of:(1)flue gas blower(B-200),(2)make up water control valve(EV-300),(3)wash water control valve(EV-420),and(4)wash water pump(P-420).The algorithm used in the module of wash waterflow rate into absorber(FC-420)is represented in a function block diagram,which consists of the function blocks of data input simulation,data output simulation, and primarily a PID control function.Since the KBSCDC system is not connected to the CO2capture plant at the current stage,the data input and output to the system are simulated by using func-tion blocks.The PID control function block contains the most important parameters,which includes the set-point(SP)of the PID control and alarm activation limits,whose variable names in the system are HIGH_LIMIT and LOW_LIMIT.The details of system construction and implementation are given in Section4.2.More implementation details about the knowledge hierarchy of the KBSCDC in DeltaV Simulate are explained as follows.Fig.5 shows how the system knowledge base was developed in the Del-taV system.In Fig.5,the white boxes on the left side contain the components of the knowledge base.As observed vertically from the top to bottom,the components of the domain knowledge con-sist of objects,the attributes of the objects,and values of the attri-butes.The blue or shaded boxes on the right side contain the components of the DeltaV Simulate.As observed vertically,the components of the DeltaV Simulate are also displayed from the higher to lower hierarchical level from the top to bottom(refer to Fig.3).More details on how the knowledge components are rep-resented as the components of the DeltaV Simulate at different lev-els are given as follows:Level1:The objects of the plant include the reaction instru-ments,pumps,and valves.The reaction instruments are defined as the plant area in DeltaV Simulate.Since the CO2capture plant contains16reaction instruments,there are together16plant areas defined in the system.Level2:As mentioned in Section3.1.1.2,the attribute of location of a valve or pump determines the plant area to which a valve or pump belongs in the system design.Each plant area can consist of a number of valves and pumps which manipulate multiple attributes of this plant area.Therefore,the objects of pumps and valves are defined as modules under the level of plant area in the DeltaV Simulate,although they are at the same level of objects as the reaction instruments in the knowledge base.As mentioned in Section3.1.1.2,the pumps and valves have two different control mechanisms of PID control and two-state control.Therefore,the pumps and solenoid valves based on a two-state control mechanism are defined as two-state control modules;the PID control valves are defined as proportional-integral-derivative or PID control modules.Level3:The function block diagram(FBD)is a diagram that con-tains multiple interconnected function blocks.However,since FBD represents a type of algorithm and is not directly related to the knowledge base,it is not shown in Fig.5.Level4:A function block is a logic processing unit that defines the behaviour of an algorithm for a particular module.Two types of function blocks are available:the two-state function block and the PID control function block.At this level,the attri-butes of the objects in the knowledge base are analyzed.As mentioned in Section3.1.1.1,the attributes of a reaction instru-ment include the attributes of the reaction instrument and the attributes of its output dynamic objects,such as the pressure and temperature.Since the PID control valves are used to con-trol the attributes of the reaction instruments and the dynamic objects,the attributes of reaction instruments and dynamic objects and their relative PID control valves are combined into the PID control function blocks,which enable the present values of the attributes to approach their desired values by controlling the PID valves.As mentioned in Section3.1.1.2,the pumps and solenoid valves have another important attribute of status, which manipulate the attributes of the reaction instruments by switching between the ON/OFF states.Therefore,the pumpsTable3Diagnosis and control strategies for sample parameter of inlet-gas scrubber.Object Task Conditions Diagnosis and controlling actionsInlet-gasscrubber Control wash waterflow rate(FT-420)5.0kg/min<=FT-420<=37.0kg/minNormal operationNo action takenFT-420<5.0kg/min Warning is givenDiagnosis:The wash waterflow to water tank is low or stopped by the closed water circulationpump-420Remedial control action:Open P-420to restart water circulation between water tank and scrubber;otherwise,open up FCV-420to increase volume of water returning to water tank if P-420is open FT-420>37.0kg/min Warning is givenDiagnosis:The volume of wash water between the off gas scrubber and water storage tank is toohighRemedial control action:Close FCV-420to reduce wash waterflow from the scrubber wash water storage tank7940Q.Zhou et al./Expert Systems with Applications38(2011)7935–7946。
