Numerical representation of ontology growth as per SOV ratio.
\r\n\tThis book aims to highlight the novel and innovative techniques for designing and fabricating 3D scaffolds, their physical, chemical, and mechanical properties, the role of scaffolds on cellular behavior such as proliferation, migration, differentiation, and alignment of cells in vitro and in vivo tissue development, targeted delivery and sustain release of drugs and bioactive compounds, and development of acellular tissue substitutes.
\r\n\tThe scope of this book will include, but will not be limited to, following topics:
\r\n\t- Fabrication and characterisation of 3D scaffolds
\r\n\t- Incorporation of bioactive compounds on materials for biofunctionalisation
\r\n\t- Cellular behaviours on functionalized biomaterials
\r\n\t- Development of tissue substitutes
\r\n\t- Drug delivery system for targeted delivery and sustain release
\r\n\t- Acellular tissue substitutes for tissue engineering and regenerative medicine
An intelligent software needs to provide a platform‐independent description for services that it renders, while providing the means by which the service is accessed. Then a delivery platform is needed as such where descriptions of services are made and shared and delivered to clients also in an independent platform. The delivery platform should be able to employ a standard ontology that consists of a set of basic classes and properties for declaring and describing services. Ontology Web Language (OWL) seems to be the best candidate for this from the ontology structuring mechanisms point of view. OWL provides an appropriate representation language framework within which to do this.
This chapter provides formal methods for design and proof of an upper ontology, which is of crucial importance for e‐service composition framework. It discusses the definition of ‘ontology’, ‘service ontology’ and ‘life‐event ontology’. It gives details of what the concepts of service and service ontology mean in this study. This study explains and illustrates the overview of conceptual design for life‐event ontology through the annotation used in the formal method using first‐order logic to establish the main axioms and design rules for the ‘life‐event ontology’ [1].
Ontology described in this chapter will provide technical support for implementation of life‐event ontology‐oriented service composition platform. Life‐event ontology is a logical extension of Ontology Web Language for Services (OWL‐S), extending this existing approach enables the design of a life‐event metamodel, which in turn is used as an alterable workflow for composite services. The resulting ontology covers specific web services with semantic concepts to implement the conceptual life‐event framework in the context of e‐service composition by
Facilitating the construction of alternative integrated service workflows from entirely different web service vendors.
Enabling the repair or reconfiguration of life‐event workflows in runtime.
The invocation of web services according to the workflow sequence.
In the domain of information technology, the term ontology is a word borrowed from philosophy that refers to the science of describing the kind of entities in the word and how they are related [2]. According to the Merriam Webster online dictionary, it is defined as1
A branch of metaphysics concerned with nature and the relations of beings.
A particular theory about the nature of being or the kind of things that have existence.
In the context of information technology, the most typical kind of ontology has a taxonomy and a set of inference rules [3]. On the other hand, a more formal definition of ontology is given by Maedche [4] as follows:
An ontology is a five‐tuple as
where C is a set of classes (concepts) and R is a set of relations.
Therefore,
And,
In addition to these definitions, ontologies are used to describe a variety of models, ranging from simplest taxonomies such as Online Directory Project2 to very complex knowledge models written in first‐order logic.3
The resource description framework (RDF) is a recommendation of W3C specifications, originally designed as a metadata model. It is used as a general method for conceptual description or modelling of information that is implemented in web resources, using a variety of syntax formats.
RDF has an eXtensible Markup Language (XML)‐based syntax, it provides a metadata model for ontology knowledgebase and also provides a common framework so that applications can process and exchange the information automatically through the World Wide Web.
RDF schema (RDFS) is the description of RDF language. It is also expressed in XML and provides a simple ontology of RDF concepts and property definitions. RDF provides the basis on which next generation ontology languages are developed. In other words, most new ontology languages are logical extensions of RDF.
RDF identifies objects using Unified Resource Identification (URI) [5]. The literature describes a URI to be a series of characters that identifies an abstract or physical resource. URL is used to identify resources (available date) on the Internet, these resources be in form of html web pages or other form of data made available in the XML form. RDF uses simple property terms and their values to describe resources. RDF statements are represented as triples consisting of subject, predicate and object. The subject signifies the resource, the predicate represents the relationship between the subject and the object. The object can itself be a resource or a string literal, which represents a basic data type such as Integer or Boolean values.
RDF schema as the semantic extension of RDF describes and maps out all the relationships between resources in RDF. The resources are divided into groups or classes, which they provide a simple hierarchical classification structure, which relates these classes to each another through their properties. There is also ‘Predicate’ or property element, which represents the predicate and object of the statement. Its content is the object of the statement, which described as plain literal.
Recent advances have resulted in new developments in RDF towards a more cohesive approach towards new representation of knowledge. One of the most talked about is Ontology Web Language (OWL). OWL ontology is also an RDF graph and is represented by a set of RDF triples. As with any RDF graph, an OWL ontology graph has different syntactic forms. RDF and RDF schema ontologies are extended by OWL via adding more terminology for describing properties and classes as well as cardinality, equality, relations between classes, richer property typing, property characteristics and enumerated classes.
Pure hierarchy of classes and their relationships is not the only thing that the ontology is defining; they are also used to inference class relationships such as equivalence or being disjoint.
The root class of OWL ontology is called owlThing. Other classes defined within the ontology are subclasses of owlThing. OWL also supports a set of operators on classes such as union, intersection and complement. It also allows class enumeration and disjoint. There are two types of simple properties in owl description: one is ‘data type’ and the other one is ‘object’ properties. Relationship between class instances and RDF literals or XML schema data types is defined as data type properties. Relationship between the instances of classes is defined as object properties. There could also be logical connectivity such as transitivity, inverse and functional and symmetric connections. Similar to RDFS, an OWL class can contain instances of individuals of the class and other subclasses [6].
According to RDF, instances are defined to be the descriptions of a class with certain values in their properties. On the other hand, in OWL a class could also be defined with logical restrictions based on some properties. Classes can also be restricted by existential or universal quantifiers. For instance, the class life event may have subclasses defined with existential quantifiers on the hasService property such as ‘hasService some Life‐eventService’ [1].
Hence, to restrict a subclass of the life‐event class, one can define it as the MotorcycleLicenseLife‐event class, which contains all life events that have Life‐eventService as a service. This includes all the instances that have the hasService property assigned to the Life‐eventService. In addition, these properties can also have cardinality restrictions. Therefore, one can safely say life event must have at least one Life‐eventService but may have more than one. Thus, the hasService property can be restricted to
hasService > 1 and a life‐event instance can have multiple hasService properties.
OWL has three sublanguages: OWL Full, OWL DL and OWL Lite, all described in Ref. [7]. OWL Lite is the least expressive of them. OWL Lite is somewhat more expressive than RDFS, because it provides simple constraints of classes and properties in addition to supporting a classification hierarchy. OWL DL is modelled on description logics, all conclusions are guaranteed to be computable that means, it supports maximum expressiveness while retaining computational completeness. A service class does not need to be an individual in order to represent a collection of individuals. This study suggests the use of OWL reasoners such as FaCT++ [8] to check for the accuracy of OWL documents.
OWL ontology can be queried in several ways. One effective way is to use a reasoner in order to create a class with certain constrains, then by classifying the class within the ontology, one can see which classes it relates to. All other relationships of this class such as equivalent classes, super classes and subclasses can also be inspected by the query processor. Reasoners can also be used to query over the OWL instances. The query processor in this method converts the query to an OWL class instance, where all the property values are the same as that of the query, where the reasoner can find all the classes that have this instance as an inferred instance. An inferred instance is known to be one that has not been explicitly instantiated within a class but is inferred to be part of a class as a result of the values and type of its properties. There are more OWL query languages that have been developed besides these methods. One example of OWL query language is the OWL‐QL [9], which is a formal language and protocol that queries an OWL ontology by finding class relationships. It also allows querying and answering agents to conduct a query‐answering dialogue in ontologies represented by OWL.
The reasoning power of ontology is the motivation behind its use for representing services rather than using simple attribute‐value representations of data such as in traditional databases. An example of a query which can be done using an ontology which is difficult to do using a Simple Query Language (SQL) query is ‘Given a service class, find all logically related matches to my query’. Simple Query Language (SQL) also does not support abstract data types, thus making it difficult to determine whether a certain property value belongs to a number of different classes or types. Ontologies can also be shared, re‐used and changed. Ontologies can be distributed across the Internet and grow limitlessly, and they can be discovered and shared using their URI.
When new relationships are established within the ontology schema because of ontology migration or the addition of new classes, determining new relationships within the ontology is simply reduced to running a reasoner on the ontology in order to reclassify the classes. For relational databases, changes to the schema may have a fundamental impact on the existing data.
The main drawback to using ontology is that classification is expensive. As ontologies grow large, and especially when instances of classes are stored in the ontology, reasoning becomes a bottleneck. We tackle this problem by storing instances in separate ontology data‐files instead of the ontology schema itself. This speeds up the classification process considerably. Moreover, a positive side effect of the distributed architecture of Life-event Ontology Oriented Service Integration (LOOSI) is that it allows each component to handle different ontologies (OWL‐S and life event).
In order to understand what real‐world services are, we look at some works in the economic and business sciences as well as literature originating from Information and Communication Technology (ICT) area.
Hardly any of the generic concepts concerning real‐world services show up in current e‐commerce product classifications or standards for web services, a term that refers to Internet‐based technologies, rather than business activities. Web services are loosely coupled reusable software components that semantically encapsulate discrete functionality. Web services, however necessary and useful, cannot really be seen as services in the sense of the business science literature, they are currently rather restricted to input/output interface specifications.
There are many initiatives that have made progress towards defining and organising ontologies for web services; two of which have contributed a great deal to the state‐of‐the‐art:
OWL‐S describes a set of foundation classes and properties that can be used for declaring and describing services. One example of that is described in W3C 2004 documentation, stating that ‘an ontology for describing Web Services that enable users and software agents to automatically discover, invoke, compose and monitor Web resources offering services under specified constraints’ [10]. OWL‐S tries to cover the description of services in a wide sense, not focusing on a particular application domain or problem.
Web Services Modelling Ontology (WSMO) intended to solve the composition problem by creating ontology for describing different aspects of semantic web services, but with a more defined focus. WSMO also takes into account the specific application domains (e‐commerce and e‐work) in order to ensure the applicability of the ontology for these areas.
In comparison of the two standards, WSMO includes majority of the elements present in OWL‐S while adding new elements in order to grow its relevance in most domains. Mediation and compensation are examples of such characteristics that are key issues yet to be solved in order to achieve the real implementation of semantic web services, in such a way to be relevant for e‐commerce [11]. A more thorough look at WSMO reviles that it should provide a higher level of detail for the definition of aspects such as choreography or grounding required by web service implementation. If these elements are appropriately covered, then WSMO can become a strict superset of OWL‐S that also covers relevant issues not covered by OWL‐S. WSMO also intends to have an execution platform, called web service modelling eXecution (WSMX) environment, while the intentions of OWL‐S in this direction are not yet defined.
A web service transaction involves three parties: the service requesters, the service provider and a mediation infrastructure facility. The service requester, who may broadly be identified as a user, seeks a service to complete its task; the service provider, who can be broadly identified as a provider, provides a service sought by the user. The user may not know of the existence of the provider ahead of time, so it relies on mediator infrastructure facilities that act like a service registry and workflow organiser to find the appropriate provider. The role of the mediator registry is to match the request with the offers of service providers to identify which of them is the best match. In Chapters 6 and 7, we will provide the details of such facilities that can act as a delivery platform for life event and a framework for using such a platform. The remainder of the section explains the detailed design for an ontology that can provide a foundation for such a framework.
Consider the utilisation of a credit card service. A customer can choose the simplest form of a credit card or a more expensive card, which offers extra services as free travel insurance, high withdrawal limits, travel assistance abroad, worldwide card replacement in the case of loss, linking the card to a preferred supplier and many more. Multiple aspects of this service offering can be facilitated by websites: ordering a card, transactions listing, buying other services and goods with the card and more.
Another example is the online organisation of events such as conferences, board meetings, executive courses and exhibitions. Their electronic facilitation requires many capabilities, including a good predefined classification of such events, together with a description of their properties, plus the constraints they impose such as suitable times and spaces (rooms, halls and room set‐up).
Essentially, an ontology is needed that can define the core contents of the service. In addition, electronic facilities should provide the capability of selecting relevant supplementary services. Such services include travel insurance and high withdrawal limits in the credit card case, and coffee breaks, video facilities, Internet connection, translation, sound, technical assistance or catering, in the event organisation case. This, once more can only occur in a predefined and standardised ontology‐oriented way, in a way that related additional restrictions and relationships can be automatically provided for. The other issue is where service consumer requirements regarding a service usually contain some implicit things, fuzzy statements, and often requires a substantial interpretation. This steps into the service provider’s ontological terminology and the components that the service provider can actually deliver.
OWL‐S has made progress towards configuration‐based service composition. This study suggests that an important element of a paradigm for the support of real‐world services is a generic description of services and what they provide. Simply, a service ontology that the runtime design and production of services can be simplified to a configuration task. This task of looking up configuration in order to build composite services is called service composition. This can be translated in to a collaborative e‐government scenario, where the ideal is to have an intelligent support system that [12]
ontology has service bundle contents,
interprets the preferences and customer needs into suitable terms from the perspective of the service provider, and
can deal with all the associated restrictions while automatically constructing the requested service in a configuration‐oriented way, which supports the composition of services from different service providers into a user‐controlled workflow of composite web services.
The biggest limitation of OWL‐S is that it only allows for the composition of services (or operations) described in one Web Services Definition Language (WSDL). A new system of web service knowledgebase configuration would be necessary if we wanted to compose web services from different vendors. One major challenge is that the service ontology must be sufficiently generic to be useful across many application domains. We discuss below how such an ontology might look and present its logical formal description.
This research defines the life‐event ontology as the logical extension of OWL‐S to provide extended functionality, which allows a systematic composition of e‐government web services by means of abstraction in design and implementation. The advantage of such an extension is that it preserves and uses all the capabilities inherited from upper ontologies such as OWL and OWL‐S, while adding more specialised ontology concepts to achieve precise results for automating the composition of e‐services by means of abstraction. The design for life‐event ontology will take OWL‐S one step further to integrate atomic and composite services not only from one service provider but also from many, to allow the dynamic construction of a user‐controlled workflow of readily available web services.
The model for life‐event ontology requires two types of knowledge analysis in order to achieve a more comprehensive solution for the design of the ontology itself. This ontology needs to provide two essential types of knowledge about an event in a business or a persons’ life.
Life‐event ontology as a service knowledgebase is required to automate the acquisition of individual web service instances in a life‐event workflow. It provides service‐specific information such as availability, service type, service profile and required communication parameters to the runtime workflow construction process. A service knowledgebase could use multiple ontology descriptors (OWL‐S ServiceModel) to obtain the semantic information required by the workflow for the invocation of atomic services. Life‐event ontology embodies the following concepts:
Life event: a metamodel that provides a category of knowledge that is needed to answer the question ‘In what possible alternative ways can a life event be constructed?’ The answer to this question starts with the concept of life event that is the root element of the ontology inherited directly from the generic concept of thing. This ontology class is the definition of an abstract construct of all possible services that are nominated to collaborate with each other in order to solve a business problem. This ontology class has the inversed functional object property called hasService. This object property is of type class Life‐eventService. The minimum cardinality of this property is one; this means that a life event must be composed of at least one service. One of the most important responsibilities of this class is to enforce the rules of government regulations to make sure that a legally acceptable workflow is provided that can be instantiated and executed to fulfil a customer request for a service. Listing 1 is the RDF code for the construction of this object property. <owl:ObjectProperty rdf:about=”le#hasService”> <rdf:type rdf:resource=”&owl;InverseFunctionalProperty”/> <rdfs:domain rdf:resource=”le#Life‐event”/> <rdfs:range rdf:resource=”le#Life‐eventService”/> <owl:inverseOf rdf:resource=”le#describedByLife‐event”/> </owl:ObjectProperty>
Listing 1. Definition of object property hasService.
Life‐eventInstance: one possible way of implementing a life event is defined by this concept, meaning that if we consider the life event as a metamodel that only defines the types and the order of possible web services in a workflow, then a Life‐eventInstance would be one of the possible ways to create such workflow. This concept has an object property of type ServiceInstance called hasServiceInstance, with the cardinality of one. This property represents the individual invokeable instances of web services that make up the workflow of the life event at runtime. Listing 2 is the RDF code for the construction of this object property. <owl:ObjectProperty rdf:about=”le#hasServiceInstance”> <rdfs:domain rdf:resource=”le#Life‐eventInstance”/> <rdfs:range rdf:resource=”le#ServiceInstance”/> <owl:inverseOf rdf:resource=”le#partOfLife‐eventInstance”/> </owl:ObjectProperty>
Listing 2. Definition of object property hasServiceInstance.
Life‐eventService: provides knowledge about the acceptable web service types and possible sets of actual service instances for every service type. In other words, this concept is the abstract construction of all service types that could potentially be instantiated as a ServiceInstance at runtime. As it is strongly acknowledged by other research literature [13], the diversity of structures, regulations and procedures affecting networks of heterogeneous administrative units, represents a challenge for semantic composition. This type of knowledge is specifically related to e‐government service composition, since every Life‐eventService participant in any life event may enforce or be affected by one or more government regulations. These regulations are the governing rules of composite services in the e‐government domain, specifically because regulations are one of the integral parts of inter‐agency processes (i.e. where the life‐event process flow crosses multiple agencies). Furthermore, regulatory knowledge is required for designing an inter‐agency workflow that crosses the boundaries of local, state and federal agencies. It has three object properties:
hasPrerequisite provides the knowledge about the order of services in the workflow or possible required action or documentation prior to the invocation of the web service. Listing 3 is the RDF code for the construction of this object property. <owl:ObjectProperty rdf:about=”le#hasPrerequisite”> <rdfs:domain rdf:resource=”le#Life‐eventService”/> <rdfs:range> <owl:Restriction> <owl:onProperty rdf:resource=”le#hasPrerequisite”/> <owl:someValuesFrom rdf:resource=”le#Prerequisite”/> </owl:Restriction> </rdfs:range> </owl:ObjectProperty>
Listing 3. Definition of object property hasPrerequisite.
hasServiceType and (3) hasServiceSubType provide knowledge about the type of LfeEventService. Listing 4 is the RDF code for the construction of these two object properties. <owl:ObjectProperty rdf:about=”le#hasServiceSubType”> <rdfs:domain rdf:resource=”le#Life‐eventService”/> <rdfs:range rdf:resource=”le#ServiceSubType”/> <rdfs:subPropertyOf rdf:resource=”&owl;topObjectProperty”/> </owl:ObjectProperty> <owl:ObjectProperty rdf:about=”le#hasServiceType”> <rdfs:domain rdf:resource=”le#Life‐eventService”/> <rdfs:range rdf:resource=”le#ServiceType”/> <rdfs:subPropertyOf rdf:resource=”&owl;topObjectProperty”/> </owl:ObjectProperty>
Listing 4. Definition of hasServiceSubType and hasServiceType.
ServiceInstance: it is a personalised instance of the Life‐eventService and provides knowledge about the user preferences at runtime. It implements all the prerequisites of the web service that are enforced by the Life‐eventService. ServiceInstance extends Life‐eventService and represents one of many possible runtime instances of the Life‐eventService. This ontology class has a property partOfLife‐eventInstance that points to a class Life‐eventInstance described in Listing 3 as the inverse functional property of hasServiceInstance. It is through this property that we can obtain knowledge about actual web services participating in a Life‐eventInstance. It is our view that any problem a life event seeks to address can be fitted to two main areas of concern:
For life‐event users, it should describe how to ask for an OWL‐S service and what happens when the workflow is being executed. By ‘what happens’ we mean what are the particular technical and legal requirements of invoking any of the web services in the life‐event workflow.
For managing the workflow, life event uses a logical description to perform four different functions:
To create a composite service workflow from multiple services in order to perform a specific complex task. It is important to note that ‘composing OWL‐S services’ as a life‐event is different from ‘composite services’ described in OWL‐S specifications. The composite services described in OWL‐S are provided by one service provider and specified in one WSDL grounding specification, whereas a life event comprises services from totally different providers with separate WSDL grounding specifications.
To manage the status and results of executing a complete or a partial life‐event workflow of web services. This means that a citizen can request an invocation of a life event but does not necessarily complete the whole life event in one go. The user also has the ability to choose to invoke any of the operations listed in a web service descriptor.
To coordinate the activities of different service participants (requester, provider and mediator) during the course of the web service enactment.
To monitor the execution of the web service and compensate for any web service failures at runtime. This is made possible by the fact that a life event is a metamodel and it can be instantiated in many possible ways depending on the availability of different actual ServiceInstances for a particular Life‐eventService. There are two object properties of ServiceType and ServiceSubType that can provide essential knowledge about a particular ServiceInstance that enables a Life‐eventInstance and make a decision on substituting a failed ServiceInstance with a new one that is the closest match in terms of its object properties (hasServiceType and hasServiceSubType).
The ontology illustrated in Figure 1 displays the main concepts of life event. The abstract concept of life event is extended by Life‐eventInstance. Life event is a ‘metamodel’ representing a generic event in a typical citizens’ life. This concept points to one Life‐eventService. A Life‐eventService is the conceptual representation of a web service that holds information about the service type by pointing to an instance of a class ServiceType and an instance of class ServiceSubType. Life‐eventInstance is also aware of the position of its corresponding web service within the execution workflow at runtime. Life‐eventService represents one element in a possible set of ‘n’ elements that makes up the life‐event workflow.
Life‐event ontology conceptual graphs.
A Life‐eventService can be instantiated in many ways since it would be pointing to one or possibly more than one Life‐eventInstance through ServiceType and ServiceSubType concepts.
In order to lay down a legally acceptable foundation for invocation of any government web services and to attain a legal outcome of such invocation, one needs to satisfy a set of legally binding regulatory requirements. We achieve this by setting up the rule: ‘every element in this workflow must point to at least one prerequisite’. This rule is modelled as a concept called ‘prerequisite’. This new concept extends the concept ‘thing’, therefore it could represent anything including but not limited to document, payment or, in most cases, another Life‐eventService. This rule creates a linked list of services in which every Life‐eventService has an object property hasPrerequisite, which is of type ‘prerequisite’.
It is important to stress that the concept of prerequisite is very different from the property of ‘precondition’ described in OWL‐S specifications; this difference is illustrated in Listing 4. OWL‐S defines the property precondition to be represented as logical formulas like expressions as literals, either string literals or XML literals. The latter case is used for languages whose standard encoding is in XML such as Semantic Web Rule Language (SWRL) or RDF.
<Description rdf:about=”#process2”>
<hasPrecondition>
<expr:KIF‐Expression>
<expr:expressionBody>
(!agnt:know_val_is
(!ecom:credit_card_num ?cc)
?num)
</expr:expressionBody>
</expr:KIF‐Expression>
</hasPrecondition>
</Description>
Listing 5. Implementation of property hasPrecondition in OWL‐S.
If an OWL‐S process has a precondition, then the process cannot be performed successfully unless the precondition is true. The difference between the precondition properties of OWL‐S and the prerequisite concept of life event falls into two major areas:
Consider a process that charges a credit card. The charge goes through if the precondition (card is not overdrawn) is true. If it is overdrawn, the only output is a failure notification. This means that the precondition is an expression of whether to allow the invocation of a service to go ahead, whereas the concept prerequisite of Life‐eventService extends the concept thing, therefore it could be anything, including another Life‐eventService. A Life‐eventService can be invoked if and only if the prerequisite is not itself and the property isFulfilled of the prerequisite service is set to true.
The other important difference between the two concepts is that the concept of precondition is confined within the domain of one web service, whereas a prerequisite goes beyond the domain of one web service and includes a much larger area of the universe of discourse under the domain of life event. In next sections, we will explain the significant role of the concept prerequisite where it is of the type Life‐eventService. Listing 6 is a snippet of the owl tag for the class prerequisite.
<owl:Class rdf:about=le#Prerequisite”>
<rdfs:comment>
This class could constitute anything and is the root element of all classes that enforce workflow regulations for the life‐event ontology
</rdfs:comment>
</owl:Class>
Listing 6. Class prerequisite in life‐event ontology.
Catering for the possibility of service substitution in runtime is made possible by the concept Life‐eventService, which points to one or more ServiceInstance; this allows for the substitution of similar web services with some degree of similarity, depending on web service availability or user preferences at runtime.
We find it convenient to be able to speak about ontologies as objects and to have a theory of these objects. We will use a first‐order language that contains the usual logical operators and symbols: for negation, for conjunction, for disjunction, → for material implication, ⇆for logical equivalence, = for equality (≠ will abbreviate its negation), for the universal and for the existential quantifier. In due course, we will introduce non‐logical symbols for the relevant predicates and relations if required. We shall use x, y and z as variables ranging over existing entities, and a, b and c will be constants denoting such entities. We will use ω and ω′ as variables ranging over ontologies, and α, β and γ will be constants denoting ontologies.
We do not offer a full logic, and in particular, there will be no consideration of a deductive system. For the rest, unbound variables are assumed to be within the scope of universal quantifiers.
Life‐event candidate is an abstraction of a complex workflow consisting of a number of composite or simple web services. Life‐event candidate can use OWL‐S descriptors of web services provided by service provider including the required government regulatory information, which is required to guarantee a legally acceptable outcome whenever the service is executed.
In the following, we provide a formal description for the main concepts (classes) of our life‐event ontology followed by their description logic:
Life‐event = LE
Life‐eventInstance = LEI
Life‐eventService = LES
ServiceInstance = SI
Prerequisite = PR
ServiceType = ST
ServiceSubType = SST
We shall use the predicate ‘concept’ in order to denote concepts, thus ‘concept(x)’ is to be read, ‘x is a concept’, and a concept can be from any of the following types: (LE, LEI, LES, SI, PR, ST and SST). We will use x, y and z as variables ranging over concepts.
Symbolically, the first axiom is an existential one, which asserts that there is at least one entity of a certain type. Here, we indicate that there exists ontology ω and there exists entity x that is of concept (class) life event in a variable ontology ω.
We use the predicate Ω in order to denote token, thus ‘Ω(ω)’ is to be read ‘ω is an ontology’. An instance of a given ontology token α is an entity whose existence is recognised by α. We will write ‘inst(x, α)’ which is to be read ‘x is an instance of α’. Hence, there is no empty life‐event ontology.
In addition, any existence is a constituent of ontology.
The predicate ‘realises’ denotes the instances of associated LES concepts within the life‐event ontology. While each instance may be a model on its own, a combination of LESs may be aggregated to constitute a composite model. In that case, the services are considered to be the components of a model. LES (y) play role (r) that requires skills (s) needed to perform their role.
Given that LES provides a set of specific operations O and x is a variable over this set to fulfil a t that is a variable over the set of tasks T, it would be required to have a subset S’ from the skill set of S.
We must ensure that every LES carries enough semantic information to facilitate the runtime reconfiguration in the case of a web service failure during the LEI execution. Every LES has an object property ‘hasPrerequisite’ that makes one LES the prerequisite service of the value of this property in the workflow of LESs. Each of these object properties points to another LES, essentially creating a linked list of LESs in which every LES is aware of its place in the list through the data property called WorkflowPosition. In other words, a life event is the construct of a two‐dimensional linked list, in which the first dimension is the list of meta‐services and the second dimension is the list of service instances for each meta‐service.
There is no restriction on the complexity of the logic that may be used to state the axioms and definitions of concepts in ontology. The distinction between terminological and formal ontologies is one of degree rather than kind. Life‐event ontology tends to be smaller than terminological ontologies, but its axioms and definitions can support more complex inferences and computations. We conduct the experimental evaluation of the life‐event ontology in two stages. We use the ontology editor tool Protégé to design and develop the life‐event ontology schema as the preparation for evaluating the life‐event ontology. We use the FaCT++ reasoner plug‐in from within Protégé to perform structural validation of the schema. To evaluate the efficiency of life‐event ontology, an experiment is conducted to measure the complexity of the ontology through a set of well‐known formal methods and demonstrate the results in a numerical as well as graphical representation. We compare the life‐event ontology to OWL‐S ontology since it is the most conceptually similar to it.
As ontologies grow in size and number, it is important to be able to measure their complexity quantitatively. Quantitative measurement of complexity can help ontology developers and maintainers better understand the current status of the ontology, therefore allowing them to better evaluate its design and control its development process. We are using a suite of ontology metrics [11], at both the ontology level and the class level, to measure the design complexity of life‐event ontology. This ontology complexity measurement metric was evaluated in an empirical analysis on public domain ontologies to show the characteristics and usefulness of the metrics. The proposed metric suite is useful for managing the life‐event ontology development projects.
We use three different ontology level metrics to measure the complexity of the ontology:
Size of vocabulary (SOV) measures the amount of vocabulary defined in ontology. Given a graph representation G = (N, P, E) of an ontology, where N is a set of nodes representing classes and individuals; P is a set of nodes representing properties and E is a set of edges representing property instances and other relationships between nodes in the graph G. In this measurement, SOV is defined as the cardinality of the named entities
Edge node ratio (ENR) measures the connectivity density. In this measurement, ENR tends to increases as more edges are added between nodes. The greater the ENR, the greater the complexity of an ontology. ENR is calculated as follows:
as the division of the number of edges
Tree impurity(TIP) measures how far ontology’s inheritance hierarchy deviates from being a tree and it is defined as being:
where
This metrics are mostly concerned with the class level specific statistics, the most popular technique in this method is known as number of children (NOC), as such to calculate NOC for a given class C, NOC measures the number of its immediate children in the ontology inheritance hierarchy given, as follows:
where
This section will describe the preparation for a comparative evaluation of life‐event ontology against OWL‐S using the methods described in Sections 6.2.2 and 6.2.3.
Step 1: Building the ontology. It aims to prepare for the evaluation of the life‐event ontology. The choice of ontology editor was made mainly due to the fact that Protégé is open source software and was more suited to our purpose [14]. This tool is developed and maintained by Stanford University. We used version 4.1, which was more advanced, intuitive and easier to use than other available ontology editors. Figure 2 shows a screenshot of our developed ontology, which illustrates the concepts, their relationships with object properties and data properties in the Protégé ontology editor.
Life‐event ontology built by Protégé.
Step 2: Comparable ontology. This step is to select an ontology that is conceptually similar to life‐event ontology. OWL‐S is chosen because it is not only conceptually very similar to the life‐event ontology but also functionally designed to perform a similar work. This ontology is also used by the LOOSI platform to provide knowledgebase support for the web service enactment functionality of the system. The diagram shown in Figure 3 is the graph representation of OWL‐S conceptual schema version 1.1.
OWL‐S ontology schema [10].
In this comparative evaluation of life‐event ontology with OWL‐S, we use numerical value results from applying the metrics in Sections 6.2.2 and 8.2.3 on both ontologies to illustrate the measurement of efficiency and complexity of the life‐event ontology in compare to the OWL‐S.
The experiment starts with creating and adding five named individuals that are the representatives of five individual web services that are published by the Australian Government agencies and other businesses. One more named individual is created only in life‐event ontology as the first instance of the schema to point to the Life‐eventServices. The list of these named individuals is described in Figure 4.
Life event and OWL‐S named individuals.
Considering the populated OWL‐S ontology and the life‐event ontology, we use the actual measurements with the methods described in Sections 6.2.2 and 8.2.3 and calculate the results as follows:
Based on the SOV method, we measure the SOV value of the life event to be 7 + 8 = 15, and for OWL‐S to be 12 + 9 = 21, this means that if we consider the growth ratio for the life event as being the base 15/15 = 1 then this for the OWL‐S would be 21/15 = 1.4. Table 1 details the numerical representation of the growth of ontology data in both ontologies.
Life event | OWL‐S |
---|---|
15 | 21 |
30 | 36 |
60 | 86.4 |
120 | 207.36 |
240 | 497.7 |
Numerical representation of ontology growth as per SOV ratio.
We register the statistics in Table 1 by assuming the initial SOV as to be the sum of the nodes, plus object properties in the ontology schema. Then we increased this number five times as per the number of named individuals representing the web services created for this experiment, each time by the amount of SOV ratio, representing the linear growth in the volume of ontology data.
Figure 5 shows the comparative graph representation that illustrates the trend of growth in the life‐event ontology data and the OWL‐S ontology data. It is shown that the rate of growth in the volume of data in the life event is dramatically less than the OWL‐S, after a fivefold increases in the number of named individuals.
Physical representation of growths in ontology data as per SOV ratio.
Based on the ENR method, we measure the ENR value of both ontologies in question to be as follows:
Table 2 shows the growth of ontology data in both ontologies in terms of ENR in numerical terms. The statistics shown in Table 2 is obtained by initial ENR 1 is increased five times as per the number of web service named individuals, created for this experiment, each time by the amount of ENR ratio.
Figure 6 shows the comparative graph illustrating the trend of growth in ENR for the life‐event ontology and OWL‐S ontology in the event of growth in ontology data. It is shown that this increase in life‐event ontology is less than OWL‐S after a few fold increases in the number of named individuals.
Based on the TIP method, we measure the value of ‘how far life event deviates from being a tree?’ to be (15 − 7 + 1 = 9), and for OWL‐S to be (21 − 8 + 1 = 14). As it is shown that this value is greater for OWL‐S than for life‐event ontology.
Using the NOC method, we calculated the number of immediate children (rdf: subClassOf) for the class Parameter that is the most frequently used in web service invocation to be 3. The value of NOC calculated for Life‐eventService, which is the most used class in life‐event ontology, is 2. Table 3 shows the complexity growth of ontology data for class parameter in OWL‐S in comparison with the class Life‐eventService in life‐event ontology in terms of the NOC ratio. The statistics shown in Table 3 is obtained by initial NOC 1 increased five times as per the number of web service named individuals, created for this experiment, each time by the amount of NOC ratio.
Physical representation of growths in ontology data per ENR ratio.
Life event | OWL‐S |
---|---|
2.5 | 2.63 |
6.25 | 6.9 |
15.63 | 18.2 |
39.1 | 47.8 |
97.75 | 125.8 |
Numerical representation of physical growth for ontology as per ENR ratio.
Life event | OWL‐S |
---|---|
2 | 3 |
4 | 9 |
8 | 27 |
16 | 81 |
32 | 243 |
Numerical representation of physical growth for ontology classes as per NOC ratio.
Figure 7 shows the comparative graph illustrating the trend of growth in complexity as per the NOC ratio for class parameter in OWL‐S in comparison with the class Life‐eventService in life‐event ontology in the event of growth in ontology data. It is shown that this increase in life‐event ontology is less than OWL‐S after a few fold increases in the number of named individuals.
Physical representation of growths in complexity of ontology data as per NOC ratio.
In this chapter, we outlined our design strategies to extend the OWL‐S in order to propose life‐event ontology as an abstract design and execution unit for composing e‐services. We introduced an ontology that accommodates the concept of life event within the process of e‐services composition. The idea was to introduce an innovative approach towards the whole process of e‐service composition and delivery.
We put forward a formal design for an ontology knowledgebase to manage the workflow of composite web service workflows in a linear approach. Nevertheless, this research also recognises that more research is required to specify and formalise the design of more complex types of web service composition such as parallel service processes in complex workflows.
Light emitting diodes are rapidly developing in light output, color rendering, efficiency, and reliability. Achieving good level of maintenance-free in harsh environment, while keeping product competitive, is the largest challenge which only few manufacturers manage to achieve. The latest high quality LED technologies are already exceeding all other available technologies by all technical parameters. According to its numerous advantages, even higher initial cost quickly pays for itself due to vastly reduced cost of electricity and maintenance. But to fully benefit from the outstanding advantages it is important to educate and recognize the difference between low quality and latest state of the art LED technologies, since low quality LED alternatives have quickly spread all over the world [1, 2].
LED lights use 40–80% less electricity and have at least 5 times the life expectancy than regular High Pressure Sodium (HPS) fixtures. LED lamps are 7 times more energy efficient than incandescent and twice as efficient as fluorescent lamps.
LED lights with a lower lumen output can replace conventional lamps with a higher output. For example, a 30 W LED street light can often replace an 80 W High Pressure Sodium lamp. The reason for this is directionality. LED street lamps are very directional and the light output is much more than other street lamps. Also there is little or no hot spot under the LED lamp. The light emitted from the LED lamp is directed downwards, spread throughout the entire area it covers. This means that a lower amount of light is needed to properly illuminate the area. This also dramatically reduces glare and light pollution which affects the mood of human beings, navigation in birds and insects, mating behavior in animals and flowering in plants.
LED lights last much longer than conventional lamps (4 to 8 times longer). This result in less expense in replacing the lights themselves but also the labor to replace the lamp is needed less often. This provides a great cost savings by itself.
Also the loss of brightness or lumen depreciation is slower over the life of an LED lamp than that of sodium or other lamp. So not only does the LED have a longer life span than the conventional lamp, but it stays brighter longer than other lamps. The long life span reduces maintenance expenses and makes these bulbs particularly suitable for difficult to reach locations and for streetlights where maintenance costs can be significant.
Lifetime and Lumen maintenance compression between LED and HID lights is illustrated in Figure 1. The comparison shows that relamping of HID fixture is required to be done 5 times to achieved one base life time of LED, considering the relamping is required when the Lumen reduces to 70% of initial lamp Lumen [1].
Lifetime and lumen maintenance compression between LED and HID.
LED operates at efficiently at low and high temperatures, and unaffected by on/off cycling. This makes them safer and efficient in special indoor applications such as refrigerator lights, cold room lights, offices, industrial plants and better for applications requiring frequent switching on and off lights. These bulbs are shocks and vibrations resistant making them the best choice for places like offshore platforms, oil refineries, steel factors, skids and similar applications.
The light is easily controllable with intelligent systems. The light can be turned on and off instantly and can be dimmed for added energy savings at dawn, dusk, and also during hours of low traffic. Switching on–off and dimming does not affect the life-time of the luminaire as in the case of fluorescent lights.
The carbon footprint of LED street lights is smaller than other lights due to lower energy usage. Moreover, LEDs last 4 to 8 times longer than any other bulbs, further reducing the carbon footprint of manufacture over the life time. From another angle, wide range application of LED in a country my give better chance to sale there international quota in CO2 emission to other countries.
Because of the directional light, light is carefully distributed exactly where it is meant to go and therefore there is no or little light which is wasted by illuminating the night sky or very low background light contribution. This is a considerable plus especially if the local community has a Dark-sky Initiative.
It is worth to mention here that for example LED street lamps with color temperature 3.500–4.200 K are rendering more natural light than the yellow of sodium lamps or green of fluorescent streetlights. Also no UV or IR radiation is emitted from the LED street lamps. Color rendering index (CRI) is high (80–90) and displays natural colors of illuminated objects. This reflect actual color of the objects.
LED luminaires contain no harmful substances, like mercury, lead or other hazardous chemical and gasses. Spent LED lamps can be thrown away without any special handling or disposal requirement, since they are recyclable and environmentally friendly. Other lighting bulbs often have hazardous materials such as lead and mercury which require special handling and waste management procedures which have both economic and environmental costs.
European Commission issued the Regulations EC No. 245/2009 for tertiary lighting products on 18 March 2009. On the basis of these Regulations, about 1 billion lighting products have to be replaced by LED type by the year 2015 only in the area of the EU, which translates to 100 million street lamps for street lighting and industry. The remaining 900 million refer to neon lamps.
Similarly, the Energy Information and Security Act of 2007 began the process of restricting the sale of inefficient lamps in the US. By 2012, with a few exceptions, the result of the legislation will be that inefficient incandescent lamps cannot be sold. [1]
High-intensity discharge lamps (HID lamps) are a type of electrical gas-discharge lamp which produces light by means of an electric arc between tungsten electrodes housed inside a translucent or transparent fused quartz or fused alumina arc tube. This tube is filled with noble gas and often also contains suitable metal or metal salts. The noble gas enables the arc’s initial strike. Once the arc is started, it heats and evaporates the metallic admixture. Its presence in the arc plasma greatly increases the intensity of visible light produced by the arc for a given power input, as the metals have many emission spectral lines in the visible part of the spectrum.
Many lighting application use HID bulbs for the main lighting systems, although some applications are now moving from HID bulbs to LED because of the LED advantages [2].
By about 2010 LED technology came to dominate the outdoor lighting industry; earlier LEDs were not bright enough for outdoor lighting. A study completed in 2014 concluded that color temperature and accuracy of LED lights was easily recognized by consumers, with preference towards LEDs at natural color temperatures [3]. LEDs are now able to match the brightness and warmer color temperature that consumers desire from their outdoor lighting system.
By comparing the power characteristics and lighting characteristics for LED verse traditional lighting, it can be concluded that using LED lighting to replace the traditional lighting devices are possible and recommended. However, still protection circuits such as current, voltage and temperature are still needed to be revised to increase the reliability. In order to make such mission become truth, the first important thing should be done is to lower the unit cost and secondary to have a proper and reliable power circuit with less loading and less electrical faults probabilities. Also suitable optics is needed to control the light pattern from the LEDs including focus, diffusion, reflection, and light amplification. [4, 5].
For indoor Lighting, seven criteria are proposed to assess the technical and economic characteristics of LED luminaires and ensure their compliance with European Norms regarding office lighting. The proposed decision support system can be applied to any type of luminaire and can be used by professionals who want to evaluate different luminaire suppliers and determine the optimal luminaire tender for the lighting of any indoor space [6].
Other researches concentrated in Road lighting to compares mainly the life cycle costs (LCCs) of two typical alternatives in current road lighting: the HPS and LED luminaires. These studies have considered only the road lighting design criteria, but the esthetics and visual attractiveness are excluded from the comparison. The comparison and the results have considered only the direct energy operating cost. [7]. Also an Economic cost analysis comparison between LED and HPS flood lights for an outdoor design, but using solar PV as a power supply, has been carried as a part of renewable energy design [8].
Feasibility study of LED lamp in replacing the conventional fluorescent lamp was conducted. Analysis and comparison have been carried out on the two lighting systems in terms of electrical and photometrical performance. The study did not cover any HID outdoor lighting [9].
Comprehensive techno-economic analyses that considered the Company and National economic benefits that can be achieved from the high service life of the LED light fittings (up to 100,000 Hours) and its low power consumption compared with HPS was carried out. However, this analysis is limited only for 400 W HPS lighting case.
For the above survey, it can be found that several efforts carried out economic analysis of replacement different light fittings with LED. But, none of these works has considered the economics for replacement the HID lamps by LED lamps in industrial plants. Moreover, none of these researches have presented any type of economic index to support such type of lighting projects, except [10], which limited the research the replacement of only HPS type used in access road of a gas production company.
Based on the above survey, the first goal of this chapter is to discuss the economic benefits of replacing outdoor different type of HID lights with different rating installed in an oil and gas plant, as typical “Case-Study” for industrial plant, with suitable equivalent number of LED lighting fittings, to provide even better lighting effect level, without changing the lighting poles. The second goal is to determine the global saving norm based on two main aspects. “Company Benefits”, in which the Company can gain it directly, and “National Benefits” that can be achieved by creating better gas sales opportunity for the county and by the reduction of the CO2 emission and hence the pollution.
In this section, firstly, comprehensive economic study is introduced to replace 241 pieces of 150 W Metal Halide, 103 pieces of 400 W HPS lighting, 20 pieces of 1000 W MH lighting and 162 pieces of 70 W Bollard lighting by equivalent number of LED lighting fittings. Next, economic discussion is to carried out to provide four important economic indicators. Finally, summary, conclusion and recommendation are given.
The methodology in this economic study is carried out to estimate the financial benefits of replacement of outdoor HID (High intensity discharge) lights in an oil and gas plant by the equivalent LED (Light Emitting Diodes) lighting fixture. The Study has considered the following factors:
Company (Direct) Benefits:
The initial cost of the replacement the lighting fixtures.
The energy saving.
The maintenance cost.
National (Indirect) Benefits:
Natural Gas Sales opportunity
Pollution Cost
In Company Benefits, calculation for “Luminaire Cost”, “Power Consumption” and “Maintenance Cost” are given based on offers and prices collected on 2015–2016 from different bidders, contractors and suppliers to find the lowest prices.
In National Benefits, two benefits are considered. First benefit is the gas sales opportunity that will be gained from the reduction of the power consumption in case LED light is used. Natural gas valued using the wholesale price of $4.618/MMBtu based on US Energy Information Administration Henry Hub/NYMEX futures prices; Equivalent energy rate of 5.6 ¢/kWhr is used to value the energy produced over 10 years, assuming 1% annual escalation factor and Euro to USD exchange rate of 1.2 [10, 11] Accordingly,
Where Δ kWhr is the reduction in the power consumption.
However, the second benefit is the cost saving due to the reduction of the CO2 emission, and hence less pollution. Carbon credits based on current market is typically 6 euro/ton. Where, CO2 emission is considered to be 0.83 kg/kWh. Assuming Euro to USD exchange rate of 1.2, the annual saving in pollution reduction can be calculated as following [10, 11]:
The economic study is categorized based on HID lamp type that is needed to be replaced in the plant under the study. Typical study is summarized in the following Table 1 for 150 W Metal Halide luminaire replaced by 65 W GREE LED luminaire. Where.
Company Benefits | ||||
---|---|---|---|---|
I. LUMINAIRE PRICE ANALYSIS | ||||
S/N | Description | 150 W MH Metal Halide [12] | 65 W CREE LED [13] | Remarks |
1 | Initial Fixture cost | $227.52 | $449.59 | |
2 | Total quantity | 241 | 241 | |
3 | Total quantity Cost | 0 | 108351.4986 | |
4 | Cost/lamp manpower, crane, dumping etc.… | 108.9918256 | 108.9918256 | This estimate taking into consideration replacement cost, man power, vehicle, manpower to divert/block traffic, cost of loading/unloading & installation |
a | Therefore initial investment for LED | 0 | $134,618.53 | Additional investment for using LED luminaire. |
II. POWER CONSUMPTION ANALYSIS | ||||
S/N | Description | 150 W MH Metal Halide [12] | 65 W CREE LED [13] | |
1 | Wattage per fixture | 150 | 72 | System Wattage includes losses |
2 | No of fixtures in the lighting circuit | 241 | 241 | |
3 | Total power consumed (kW) | 36.15 | 17.352 | |
4 | Hence total Power consumed per year(kWHr) | 145142.25 | 69668.28 | Average daily operating time is considered 11 Hours |
5 | Cost per kWHr | 0.026948229 | 0.026948229 | As agreed with Utility |
6 | Annual cost | 3911.326574 | 1877.436755 | |
7 | Service Life Range | 16,000–20,000 | 60,000–100,000 | |
8 | Average Service life (Hrs) | 18,000 | 80,000 | |
b | Therefore the saving in 10 Years | $20,338.90 |
S/N | Description | 150 W MH Metal Halide [12] | 65 W CREE LED [13] | Remarks |
---|---|---|---|---|
Service Life Range | 16,000–20,000 | 60,000–100,000 | ||
1 | Average Service life (Hrs) | 18,000 | 80,000 | |
2 | Number of Lamps change cycle in 10 Year | 2.230555556 | 0 | LEDs have no downtime against MH lamps which fail arbitrarily |
3 | Total No. of Lamps | 538 | 0 | |
4 | Cost/lamp manpower, crane, dumping etc | 108.992 | 0 | The estimate take into consideration new lamp cost, man power, vehicle, manpower to divert/block traffic, cost of loading/unloading & installation. |
c | Therefore savings in lamp maintenance in 10 Years | $181043.6 | ||
IV. MAINTENANCE COST SAVING ANALYSIS- IGNITORS / BALLASTS / CAPACITORS | ||||
S/N | Description | 150 W MH Metal Halide [12] | 65 W CREE LED [13] | Remarks |
1 | Rated life (Hrs) | 15,000 | N/A | |
2 | Life in 10 years | 2.676666667 | N/A | |
3 | Total No. of Ballasts | 241 | N/A | |
4 | Thus component to be replaced in 10 Years | 645 | N/A | |
5 | Cost/lamp manpower, crane, dumping etc.… | 81.74386921 | N/A | This estimate takes into consideration new ballast cost, man power, vehicle, manpower to divert/block traffic, cost of loading/unloading & installation. |
c | Therefore savings in component maintenance in 10 Years | $52,731.06 | N/A | |
SUMMARY OF 10 YEAR COST SAVINGS USING LED LIGHT FITTINGS | ||||
a | INITIAL INVESTMENT ON LED | $134,618.53 | ||
b | ENERGY | $20,338.90 | ||
c | MAINTENANCE - LAMPS | $181043.6 | ||
MAINTENANCE - COMPONENTS | $52,731.06 | |||
TOTALL COST SAVING IN 10 YEARS | $254,113.36 | |||
Total Average Annual Saving | $25,411.336 | |||
Company Net Saving in 10 Years | $119,495 | |||
Annual Company Net Saving | $11,949.5 | |||
National Benefits | ||||
V. Natural Gas Opportunity Cost: | ||||
d | Natural gas valued using the wholesale price of $4.618/MMBtu based on US Energy Information Administration Henry Hub/NYMEX futures prices; Equivalent energy rate of 5.6¢/kWh used to value the energy produced over 10 years, assuming 1% annual escalation factor. | $5071.85 | ||
VI. Pollution cost | ||||
e | Carbon credits –based on current forward market @ 6 euro/ton, CO2 emission in kg/kwh: 0.83, Euro to USD exchange rate of 1.2. | $451 | ||
National Benefit Saving in 10 Years | $5522.887 | |||
Total Benefits | ||||
Total Saving in 10 Years Operation | $125017.88 | |||
Total Net Average Annual Saving | $12501.788 | |||
Company Saving Norm = Annual Saving / kW (5) [10] | $330.55 | |||
Total Saving Norm = Total Net Average Annual Saving/kW (6) [10] | $345.83 | |||
Economic Analysis | ||||
Company Payback Period in Years (Initial investment for LED/Annual Company Net Saving | 11.27 Years | |||
Company Annual Return on Investment “ROI” in Percentage | 8.88% | |||
Total Payback Period in Years (Initial investment for LED/Annual Company Net Saving | 10.77 Years | |||
Total Annual Return on Investment “ROI” in Percentage | 9.29% |
Summary of economic study for replacement of 150 W metal halide luminaire by 65 W LED luminaire.
Similar to the typical economic study that is carried out for 150 W Metal Halide lighting, economic study is done for the remaining types of lighting; 103 pieces of 400 W HPS lighting, 20 pieces of 1000 W MH lighting and 162 pieces of 70 W Bollard lighting. Summary Tables (Tables 2–5) are provided hereinafter to show the Total Benefit and the Economic Analysis for these luminaire types.
Total Benefits: | |
---|---|
Total Net Average Annual Saving | $14,899.66 |
Company Saving Norm = Annual Saving / kW (3) [10] | $184.02 |
Total Saving Norm = Annual Saving / kW (4) [10] | $360.29 |
Economic Analysis | |
Payback Period in Years | 4.632 Year |
Annual “ROI” in Percentage | 21.59% |
Replacement of 400 W HPS lighting with (100–130) W CREE LED.
Total Benefits: | |
---|---|
Total Net Average Annual Saving | $2543.09 |
Company Saving Norm = Annual Saving / kW (3) [10] | $ 107.15 |
Total Saving Norm = Annual Saving / kW (4) [10] | $127.15 |
Economic Analysis | |
Payback Period in Years | 10.93 Years |
Annual “ROI” in Percentage | 19.15% |
Replacement of 1000 W MH lighting with (426) W CREE XAX LED.
Total Benefits: | |
---|---|
Total Net Average Annual Saving | $9291.03 |
Company Saving Norm = Annual Saving / kW (3) [10] | $799.05 |
Total Saving Norm = Annual Saving / kW (4) [10] | $819.31 |
Economic Analysis | |
Payback Period in Years | 8.657 Year |
Annual “ROI” in Percentage | 11.55% |
Replacement of 70 W bollard lighting with (34) W CREE EDGE LED.
Project Total Investment | $339,550.41 |
---|---|
Project Total Average Annual Saving | $39,235.9 |
Project Total Payback Period (Average) | 8.654 Year |
Project Annual Return on Investment “ROI” % | 11.55% |
Project economics.
Base on the above techno-economic, following Table 5 is developed to summarize the main project economics indicators that can be used as good guide line for future similar projects that consider the replacement of HID lighting by LED Lighting.
Based on the Saving Norm calculated for individual luminaire type in the above from Tables 1–4 the Global Saving Norm can be calculated based on the following Eq.:
Where “n” is the number of replaced lighting types in the study.
Using Eq. (7), the calculated Global Company Saving Norm is (355.19$/kW).
From Table 2, it can be concluded that replacement of HPS lighting by LED lighting have the highest Total Net Average Annual Saving. Therefore, it is highly recommended to use LED lights instead of HID lights in industrial lighting applications.
It is also observed from Table 4 that replacement Bollard Light Lamps by LED Lamp has highest economic value because of the very short lifetime Bollard Light Lamps compared with LED lifetime.
In Table 4, project main economic indicators are illustrated with very attractive total payback period of 8.654 years and Project Annual Return on Investment of 11.55% which is higher approximately 10 times than the international bank rate for dollar deposit. This indicator supports the decision of investment in such scope of work.
In this Section, comprehensive economic study is carried out to calculate the Global Saving Norm for the replacement of High-intensity discharge lamps with different types by LED lamp in an Oil and Gas plant, which includes also the operational cost per year. The study considered Company direct benefits and National indirect benefits in evaluating project economic indicators and in calculating the Global Saving Norm as well. The result is compared and validated with previous research effort. Four important economic indicator were provided in this Section; Global Total Saving Norm ($433.37/kW), Global Company Saving Norm ($355.19/kW), typical total payback period of (8.654 year) and typical Project Annual Return on Investment of (11.55%). These four figures are important for both project decision makers and for cash-flow controllers.
In Section 2 of this chapter, comprehensive economic study is carried out to calculate the Global Saving Norm for the replacement of High-intensity discharge lamps with different types by LED lamp in an Oil and Gas plant as “Case Study” representing industrial plant. The analysis considered Company direct benefits and National indirect benefits in evaluating project economic indicators and in calculating the Global Saving Norm as well. Four important economic indicator were provided in this Section; Global Total Saving Norm ($433.37/kW), Global Company Saving Norm ($355.19/kW), typical total payback period of (8.654 year) and typical Project Annual Return on Investment of (11.55%). These four figures are important for both project decision makers and for cash-flow controllers.
Various road classifications are existed in terms of traffic flow. Principal arterials, minor arterials, rural collectors, local roads and very low-volume roads. The last is what our concern in this section. Statistically, for low-traffic roads the flow rate of the vehicles is assumed to be 400 vehicles per day [14]. In these roads, even simple lighting system is not installed mostly, and authorities rely on vehicle lights to illuminate the roads, which putting people life and valuable product passing in these roads under the risk. The main reason of non-lighting system is the desired of saving electrical energy. The main reason of non-lighting system is the desired of saving electrical energy. However, continuously lightened fully roads cause wastage of electricity, as only one vehicles may appear every three or four hours and even more during the night time. Each of these two scenarios are contradicting and are extremely significant issues.
Several researchers did some projects and published their work related to this topic, however, none of them has considered the lighting automation system on low traffic road. Articles are mainly related to smart or automated main street lighting systems or parking areas. In the following paragraphs, several researches’ results is discussed, and main points are drawn into attention.
Some studies proposed a suggestion to use two sensors in order to consume less power with maximized efficiency of a system [15]. Light Dependent Resistor (LDR) sensor is utilized to measure the sun light intensity to control the switching action of LED streetlights, and Passive Infrared Resistor (PIR) motion sensor is used for changing the intensity of LED light when there is no motion of object in the street at mid-night, then all the streetlights are dimmed. However, [16] indicates that LDR and PIR sensor are used for same purpose, but without dimming the light, just switched on or off. In [17], the author worked on this topic using Infrared Resistor (IR) sensors which measure the heat of an object as well as detects the motion, in contrast to previous researchers did. They developed the system using Arduino Uno R3 while [18] achieved the same by Raspberry Pi 3 micro controller.
Another research effort offered Zigbee Based Smart Street Light Control System Using LabVIEW. Here, movement is detected by motion sensors, communication between lights is enabled by Zigbee technology. So, when a passer-by is detected by a motion sensor, it will communicate this to neighboring streetlights, which will brighten so that people are always surrounded by a safe circle of light [19].
Another author developed Intelligent Street Lighting System Using GSM technology. The aim is to achieve the energy saving and autonomous operation on economical affordable for the streets by installing chips on the lights. These chips consist of a micro-controller along with various sensors like CO2 sensor, fog sensor, light intensity sensor, noise sensor and GSM modules for wireless data transmission and reception between concentrator and PC. The emissions in the atmospheres would be detected along with the consumption of energy and any theft of electricity [20].
Automatic street-light control system using wireless sensor networks is also proposed in some design. The system contains lamp station and base station [21]. Each lamp station consists of Arduino Uno board as microcontroller, PIR sensor, emergency switch, LDR sensor, nRF24L01 transceiver, ultrasonic sensor, relay, LED light and a solar panel as energy source. The base station consists of Raspberry Pi as processor, nRF24L01 transceiver, and a GSM module. The automatic streetlight turns on under three conditions. Firstly, when PIR sensor detects a human or a moving object vehicle LED light is turned on. Secondly, an ultrasonic sensor is used to detect distance objects and turn on the light accordingly. Lastly, a switch is included for manual operation in case of maintenance work. The LDR sensor is included to measure the light intensity for identification of the day and night. There nRF24L01 wireless transceiver transmits the sensor information and the light status to the Raspberry web server to upload on the web page. Also, it receives commands sent from the web page to turn on or off the light at a particular node. The entire system is powered using solar cells making it more energy efficient.
The problem of high operational cost of low traffic light that use HPS lighting is partially solve by using LED light fitting instead of HPS luminaries [10].
Many real projects and researches have been done on this area [22, 23, 24], but few of them are focused in this topic exactly. Most of them consider street, campus, parking, park or any small area lighting system. The rest of them is devoted to road light and control systems. Brief analysis, discussion and comparison will be introduced hereinafter.
From the above literature review, firstly, all systems mentioned above used LDR sensor to sense night-time to operate the control system itself. In the system prosed in this Section, the same day/night sensor idea is also use to know exact hours of night-time or any dark time during the day time due to heavy cloud or any other reasons.
Secondly, all systems above have used motion sensors to detect the object movement whatever this object is, even if it is not vehicle, and hence control the lights in terms of switching ON/OFF or dimming. IR sensors and PIR sensor were the preferred sensors used to detect the object. These type of sensors detect mainly warm object and their movement. But, for the suggested system in this Section that need to be used for low traffic road, movement of only vehicle is needed to be recognized and hence switch on the light or dim them. The proposed system need to be designed to avoid any other motion such as animals, birds, or other objects which may be detected by IR or PIR sensors as this unnecessary detection of motion can cause unjustified energy consumption. Therefore, it is needed to give new approach to tackle with such problems. New approach could be to add the night vision smart camera to the system in order to recognize only the vehicles among all other objects that the camera detects.
Thirdly, some systems control the illumination by measuring the intensity of the objects movement and change the dimming of the lights accordingly. But for illumination system of low traffic roads, the intensity of the vehicles is continuously very low, and hence dimming technique is not effective solution.
Fourth, using LED light continuously operate during the night for low traffic roads can reduce the cost of illuminating the road compared with any other HID lighting, but still this is not best solution because the utilization of this system by this operational philosophy is not an efficient utilization because most of the time the light is ON unnecessary.
Fifth, in general, previous researches have been done on lighting automation system for the roads which serve both pedestrians and vehicles. But, this Section tries to design automation lighting system for long road with low traffic, where no need to switch on the lights for movement of any object except the vehicles.
In this section, efficient, safe and cost effective solution to design automated lighting system suitable for long roads with low-traffic is provided. First, description of the entire system design is discussed. Then, methodology and the programing of vehicles recognition using camera images are illustrated. Economic analysis for the proposed system is carried out. Finally, conclusion is given.
Lighting automation system in low traffic roads is intended to implement in the illuminated roads. It is supposed to have source power supply, feeder pillar with controller, light poles with day/night sensor. Such conventional system can be upgraded by new automated system. The methodology of lighting automation system in low traffic roads is achieved by applying the moving object recognition technique using cameras. Firstly, the road is sectionalized into several zones. Each zone depends on how much distance is existed between two feeder pillars, typically 400 meters. So, light poles in each zone will be switch on/off together. It means that each zone will have its feeder pillar (control panel) with controller, day/night sensor, motion sensor, and camera. Night vision cameras are installed on the road in such way to detect the vehicle arrival-to and departure-from each zone. The controller is designed to illuminate only the zones in which the vehicle is detected. The type and span of the zone are calculated based on the road design considering straight spans and roundabout.
The control scheme of the automatic lighting system is illustrated in Figure 2. Day/night switch detects darkness status to start the controller and hence motion sensor and night vision cameras. Now, let us consider that there are two adjacent zones (Zone N) and (Zone N + 1), and vehicle enters to Zone (N + 1). Mainly, day/night sensor and motion sensors of (Zone N + 1) need to be installed before the camera of (Zone N + 1), while camera of (Zone N + 1) need to be installed in (Zone N) near to the end. This is because camera need to start capture the moving objects images only after motion sensor detects any object in advance and sends the signal to the camera to start operation, and hence the controller takes the proper decision for switch the light of (Zone N + 1) before the object enter the zone.
Automatic lighting system schematic.
For that, camera is installed on a light pole about 80 m before each zone. This distance provides approximately 2 seconds for data processing and control assuming maximum speed is approximately 60 km/hour. Figure 3 illustrates the installation location of (Zone N + 1) camera, day/night sensor and motion sensors in (Zone N).
Zone definition.
The software in the controller extracts the image from the camera and analyze it to determine whether the object is vehicle or not. If the object is not a vehicle, no action is taken by controller. In case the object is vehicle, signal shall be sent to Zone N + 1 lighting feeder pillar to switch on light of Zone N + 1 Simultaneously signal shall be sent to Zone N controller to switch off lightning system of Zone.
As we explained above, each Zone has its own lighting control system consists of Day/night switch, motion sensor, night vision camera, controller and feeder pillar.
When the controller of any zone detect “vehicles” the digital counter inside this controller counts the number of these detected vehicle (Nin). In the same time, the same controller receives from the digital counter inside the controller of next Zone updated number of the vehicle interring the next zone (Nout). The communication between the controllers can be achieved by Power Line Telecommunications method. or RS-485 cable. If the difference between the these to numbers (Nin- Nout) is zero, this means that no vehicles exist in this zone, and the controller switches “Off” the light. As long as (Nin- Nout) is not zero, the light of the zone will be kept “On”. This methodology insures that the lighting system for any zone is kept “On” if any vehicle(s) still in that zone for any reason such as accident, maintenance or temporary parking. Also, this methodology insures that the lighting system of the zone free of any vehicle is “OFF”.
In Figure 4, flow chart for two consequent lighting system control logic is illustrated.
Control flow chart for zone N and zone N + 1 lighting system.
Several researches are done to recognize the vehicle at night based on vehicle lamp detection [25, 26]. This method will not work in case the vehicle lights are switches off for any reason. Another researches are carried out to detect the information in vehicle number-plate using artificial intelligent methods [27, 28]. However, using artificial intelligent method is time consuming and not useful for the application of the proposed system. In this application, recognition of the number-plate rectangular frame is simple method and more than enough to confirm that the moving object is “Vehicle”.
The process of detection of vehicle number-plate consists of the following steps: capture of image, pre-processing, plate region extraction (Figure 5).
Vehicle recognition flowchart.
In this step, the image is captured by electronic devices such as infrared digital camera or any other camera suitable for night time. The image captured is stored in JPEG format. After that the captured image is converted into gray scale image.
The next step after capturing the image is the pre-processing of the image. When the image is captured a lot of noises present in the image. Reducing the noises from the image are required to obtain an accurate result.
The RGB image is then converted into a gray scale image for easy analysis as it consists of only two color channels.
The aim of this pre-processing is to improve the quality of the image. Image enhancement techniques are used in this step. Image enhancement techniques consists process of sharpening the edges of image, contrast manipulation, reducing noise, color image processing and image segmentation.
The most important stage is the extraction of number-plate from eroded image significantly. The extraction of number-plate can be done by using image segmentation method. Mathematical morphology is used to detect the region of interest and Sobel operator are used to calculate the threshold value.
In general, any vehicle has its own number-plate which is always in rectangular shape consists characters. Accordingly, the basic approach in the detection of a vehicle is to recognize its number-plate which is mainly frame with characters (Numbers and letters). So, it is necessary to detect two criteria: the edges of the rectangular plate and there are characters within the rectangular.
A morphology based approach for detection number-plates is used. Our proposed method applies basic mathematical morphology operations like dilation and erosion.
The software model using the image processing technology is designed. The programs are implemented in MATLAB. The algorithm is divided into following parts: capture image, pre-processing, plate region extraction, characters recognition.
The following MATLAB code is written to implement the above mentioned parts:
Image capturing from camera
% Read Image
Input_image = imread(‘Car.jpg’);
RGB to gray scale
% Convert the truecolor RGB image to the grayscale image
I = rgb2gray (Input_image);
The following steps are used:
Image capturing from camera
% Read Image
Input_image = imread(‘Car.jpg’);
RGB to gray scale
% Convert the truecolor RGB image to the grayscale image
I = rgb2gray (Input_image);
Edge detection
% Sobel Operator Mask
Mx = [−1 0 1; −2 0 2; −1 0 1];
My = [−1–2 -1; 0 0 0; 1 2 1];
% Sobel Masking for filtering image
S = imfilter (I, Mx,\'replicate’);
Vertical and Horizontal Dilation
% Vertical Dilation
Dy = strel(‘rectangle’, [80,4]);
Iy = imdilate (M,Dy);
Iy = imfill(Iy,\'holes’);
% Horizontal Dilation
Dx = strel(‘rectangle’, [4,80]);
Ix = imdilate(M,Dx);
Ix = imfill(Ix,\'holes’);
% Joint Places
JP = Ix.*Iy;
ID = imdilate(JP,Dy);
ID = imfill(ID,\'holes’);
Erosion
The process of erosion reduces removing unwanted details from a binary image.
% Erosion
E = strel(‘line’,50,0);
IE = imerode(ID,E);
Filtering of digits
By filtering, the unwanted substances or noise can be removed or filtered out that is not a character or digits. Small objects or connected components should be removed and then the frame line that is connected to the digits should be identified and separated.
Bwareaopen (Image Processing Toolbox) is applied for removing all the connected components from the binary image that have value less than P pixels.
image2 = bwareaopen(image, min(numberofpixel, 100));
Stats = regionprops (L, properties) is applied for measuring a set of properties for each labeled region in the label matrix L.
stats = regionprops (image2,\'all’);
Detect plate from image
The validation of the of the number-plate recognition program, and hence the detection of vehicle, is done by two tests.
In this first test it needs to insure that the program recognizes any object, that is captured by the camera, has number plat. Therefore, the test is carried out to detect the number plat for different vehicle models and types with different orientations. The test result is illustrated in Figure 6. The program succeeded to detect the number-plate as rectangular frame include characters. It is worth to highlight here that it is not part of the program function to “read” the number-plate.
The results for objects with number-plate.
The objective of the second test is to ensure that for any object that does not have number plat, the program shall detect no number-plate. The test is done using four images for different objects consist of peoples and animals - Camels and Dog- (Figure 7). The program also succeeded to detect no number-plate.
The results with non-car images.
Any moving object enters any zone of the road shall be subject to two steps of recognition process: the first recognition process is by the motion sensor which detects that there is a moving object leaving the zone (serves-zone) and enters the next zone. The second recognition process is carried out by the image processing software that detects the moving objects which has rectangular plate with characters (Vehicle). If the two condition is satisfied simultaneously, the intelligent lighting system puts ON the road lighting of the next zoon (vehicle entering zoon) and switch off the lighting of the service-zoon after short time delay (vehicle leaving zoon).
Comprehensive economic study is carried out with the same methodology discussed is Section 2, but to estimate the financial benefits of using the proposed lighting automation system for the low traffic roads. The Study considers also Direct Benefit and Indirect Benefits [10] in order to evaluate the entire economic value of the system.
Assuming for low traffic; the vehicle flow is 400 vehicles per day [14], vehicle speed is 60 km/hour, zone distance is 400 meter, lighting pole span is 40 meter, LED fixture consumption per pole is 75 Watts [30] and electricity tariffs is typically (0.053$) per kWh [29].
From the above assumptions, flow rate of the vehicle can be calculated to be 17 vehicles per hour. Considering worst road operation scenario, at which the 17 vehicles are driven with constant speed of 60 km/hour and equal distances from each other, it is obvious to conclude that one vehicle shall enter the first zone each 212 seconds and leaving the zone (400 meter) after approximately 24 seconds. Accordingly, the zone lighting fixtures shall be switched on for 29 seconds and switched of for 183 second approximately. From that, the percentage saving in power consumption using the proposed controller compared with the power consumption when road is illuminated continuously during the night is approximately 183 × 100/212 = 86% saving.
Considering 4 km low traffic road operating for typically 50 years, Direct benefits and Indirect benefits can be calculated as following:
Considering the cost of; camera (approximate number), day/night sensor, motion sensor, controller (simplest version) [31], signal transmission between zones by RS-485 network [32], and installation [10] (lamp, manpower, crane, dumping etc.…), Table 6 can be obtained. The table illustrates that approximately $26,662.88 is needed to provide the proposed automation lighting system for 4 km.
S. No | Definition (in 4 km) | With controller | Without Controller |
---|---|---|---|
1 | Total Quantity of LED | 100 | 100 |
2 | Quantity of Day/Night sensor, Motion sensor, Camera & Controller | 10 | 0 |
3 | Unit price for Day/Night sensor, Motion sensor, Camera & Controller including maintenance | $107.40 | $0 |
4 | Total cost for item 3 | $1074.00 | $0 |
5 | Signal transmission between zones | $14,698.88 | $0 |
6 | Total Cost of Installation | $ 10,890.00 | $0 |
a | Initial Investment | $26,662.88 | $0 |
Initial investment.
Table 7 illustrates the comparison of energy consumption between using the proposed automation lighting system versus conventional system which operates all night, considering that both systems utilize LED fixtures with 75-Watt as minimum consumption for the conventional system. The table shows reduction in the power consumption of 86.31%. This reduces drastically the electrical fault probability in the lighting electrical circuits [33, 34].
S. No | Description | With Controller | Without controller |
---|---|---|---|
1 | Wattage per fixture (Watt) | 75 | 75 |
2 | № fixtures in 4 km | 100 | 100 |
3 | Total power Consumed (Watt) | 7500 | 7500 |
4 | Operating hours (hour) per day | 1.643 | 12 |
5 | Daily operating cycle % | 6.8458% | 50% |
6 | Operating hours (hour) in 50 Years | 29990.83 | 219,000 |
7 | Power consumed per day (kWh) | 12.325 | 90 |
8 | Power consumed for 50 years (kWh) | 224931.25 | 1,642,500 |
9 | Total Cost for per day ($) | 0.65 | 4.77 |
10 | Total Cost in 50 years ($) | 11,921.36 | 87,052.50 |
b | Saving in 50 years | $75,131.14 |
Energy saving.
Table 8 indicates the maintenance cost saving [10] (in terms of light fixture) such as lamp, manpower, crane, etc. for 50 years’ operation of the proposed automation lighting system and the conventional system.
S. No | Description | With Controller | Without controller |
---|---|---|---|
1 | Rated Life (Hours) | 100,000 | 100,000 |
2 | Operating hours in 50 years | 29990.83 | 219243.33 |
3 | Rate of maintenance in 50 years | 0 | 2 |
4 | Maintenance Cost per lightening pole | 108.9 | 108.9 |
5 | Total Maintenance $ | $0.00 | 21,780.00 |
c | Saving in Maintenance in 50 years | $21,780.00 |
Saving in maintenance cost.
In indirect saving, two benefits of implementing the lighting system will be drawn into attention [10].
First benefit is natural gas sales opportunity (Table 9) gained from reduction of the power consumption calculated based on Eq. (1).
S. No | Description | With Controller | Without controller |
---|---|---|---|
1 | Annual Power consumption (kWh) | 4498.63 | 32,850 |
2 | Reduction in Power Consumption (kWh) | 28351.38 | |
3 | Annual Natural Gas Sale Opportunity | $1587.68 | |
d | Natural Gas Sale Opportunity in 50 years | $79,383.85 |
Natural gas sales opportunity.
Second benefit is the cost saving due to reduction of the CO2 emission, hence less pollution. (Table 10) calculate the related saving based on Eq. (2).
S. No | Description | With Controller | Without Controller |
---|---|---|---|
1 | Annual Power Consumption (kWh) | 4498.625 | 32,850 |
2 | Power Consumption in 50 years (kWh) | 224931.25 | 1,642,500 |
3 | Annual Saving in Pollution | $1694.2752 | |
e | Saving in Pollution in 50 years | $84,713.76 |
Saving in pollution.
Table 11 summaries the calculations in direct and indirect savings. It is obvious that total saving for only 4 km road in 50 years is $ 234,238.47.
a | Initial Investment | $ 26,770.28 |
---|---|---|
b | Saving in Energy | $ 75,131.14 |
c | Saving in Maintenance | $ 21,780.00 |
d | Saving in Natural Gas | $ 79,383.85 |
e | Saving in Pollution | $ 84,713.76 |
F | Total Saving Cost | $234,238.47 |
f | Total Saving in 50 years | $ 234,238.47 |
Net saving analysis in 50 years.
To sum up, huge amount of money can be saved if such technique is implemented. In case that this system is applied to only 100 km road, total annual saving becomes about $117,119.24; total saving in 50 years becomes $5,855,961.75. It means that such system saves huge amount of energy and hence expenditure saving that can be utilized in other projects’ investment. From is discussion, it is also possible to calculates the “Saving Norm” for the proposed system to be $1171.19/km/Year (Eq. (3)).
This Section provided automation design for the illumination system for low traffic roads in order to solve the problem of operating the road not only economically but also safely. Image recognition techniques was used based on identification of vehicle number-plate to recognize the objects, is it vehicles or not? Image recognition algorithm was tested on different objects. The result from test has proved the validity of the algorithm that is used to detect different types of vehicle. Comprehensive techno-economic analysis was carried out and the result showed a great saving can be achieved, and hence, “Saving Norm” of $1171.19/km/Year was calculated for the proposed system too. This “Saving Norm” is a good index to supports project management for both project decision makers and for cash-flow controllers. The calculated value of this “Saving Norm” index encourages the implementation of this technique in any Low-Traffic Long-Roads. This index is expected to be much higher, and hence more cost saving, in case road lighting uses HID bulbs instead of LED bulbs.
The ways which are used today in order to light houses, offices, and most of indoor areas are inefficient as a lot of energy is consumed unnecessarily during the day time. This problem is also one of the design concern in Green Building. In this section, a solution to this problem and a method for people’s comfort is presented. Lights switch on automatically when there is somebody in the room and switch off when there is no occupancy. In addition to this known technique, adjustment of the brightness level of the lights will be possible via the personal computer or any other smart device. In this method, for the illumination of the lights in the area, where is needed to be controlled, light automatically is measured by sensor and considering the amount of background light coming from outside, the brightness of lights automatically controlled to reach the preset level. By the means of this method, it is possible to provide both user comfort and energy saving [35].
The energy wasting created by lighting is very significant in places where is multi-occupant, especially in offices. In Today’s world, a lot of companies provide methods in order to minimize energy consumption, because energy consumption becomes a significant problem in developing world. Many researches show that lighting system accounts for approximately 30% of energy consumption [36]. Especially, departmental stores and big offices located in city territories causes a lot of energy consumption. In offices, lighting system consume approximately twice more than printers and computers [37]. One of the main causes of this problem is that people leaves lights “on” in unoccupied places. In almost 23% of the daytime this event occurs [38]. Another problem that causes to waste of energy is called over-illumination. Over-illumination occurs when lights are brighter than needed to illuminate room. In addition to this, researches demonstrate that excessive lighting can give rise to negative health effects [38]. This problem, however, still occurs in many structures everywhere, particularly in offices. Researches indicates that lights are off for just 1 percent of daytime while the room is unoccupied [39]. And this fact shows that over-illumination occurs during daytime because of external daylight coming into the room. And, in order to overcome these problems, implementation of intelligent lighting system can be a great solution.
The direct advantage of automated lighting system is to reduce energy consumption and maintenance cost. Energy consumption is reduced, because intelligent lighting system considers external daylight coming into the room and occupancy status, hence reduce the amount of power consumed. And, maintenance cost is minimized, since lifetime of the light bulbs is better utilized and this factor extends the life span of light bulbs. In addition to this, indirect advantages of proposed solution are that it allows the country to export more oil and gas, since the consumption of fuel that is needed to generate electricity will be reduced due to the energy savings caused by intelligent lighting system. Also, a reduction in pollution can be considered as positive advantage as well, because when less energy is consumed, the amount of carbon dioxide emission released by power generation plants is reduced.
It is important to highlight that during the engineering phases of indoor lighting system, because of uncertainty of the amount of daylight and any other background light which penetrates the room, engineers ignore this factor in the design which consequently introduce several drawbacks in the operation and maintenance cost of lighting system. Typical level of illuminance for indoor lighting is given in Table 12 [35].
Facility type | Area or task type | Emin(lux) |
---|---|---|
general | Entrance halls or corridors | 100 |
offices | Typing,Writing, Reading | 500 |
offices | Technical drawing/Working on computer | 500–750 |
offices | Conference rooms/Archives | 200–500 |
restaurant | Kitchen/Dining room | 300–500 |
schools | Classrooms/Library and Laboratories | 300–500 |
hospital | Waiting rooms/Operating theater | 200–1000 |
Design average level of illuminance for various places.
It is clear from the minimum level of illuminance indicted in Table 12 for each application that the design engineer has to consider the given value as Minimum. This make the designer not only ignore any background lighting contribution, but also it considers “Minimum” illumination level that allows the designer to go to higher values to satisfy other design criteria such as symmetrical distribution of lighting inside the room. Also, this “Minimum” value of the illuminance level considered the worst calculation safety-factors that may not be applicable in all cases. Therefore, in general, most of the time in day extra unnecessarily lux level can be obtained inside the room, and hence additional money for operation and maintenance need to be spent.
For better control of the indoor lighting and reduce the operation and maintenance cost of the lighting system, there are many methods to implement intelligent lighting system in order to provide more efficient lighting [40]. First method is to use occupancy sensor in offices, homes etc. In this method, sensor is used to detect occupancy in order to control lights. If there is somebody in the room, lights switch on, otherwise lights switch off automatically. This is a good straight forward and easy method reduce energy consumption but it is not the optimum solution as the method still ignoring the contribution of background lighting, therefore it cannot be considered as high efficient way to control the indoor lighting intensity.
Second method is to utilize daylight to adjust brightness to a preset level. Energy savings are controlled by using dimming technique in which percentage of illumination of light bulbs change according to daylight coming into the room. Researches show that dimming technique reduces energy consumption up to 30% compared to non-dimmable light bulbs [41]. Daylight utilization can be accomplished by using light sensors which is used in order to detect level of illuminance inside the room and adjust brightness of the light bulbs on the basis of amount of daylight measured in the room and desired set-point. The energy saving can increase depending on the performance of light sensors used. It is reported by Electric Power Research Institute that daylight utilization can increase energy savings up to approximately 40% [42]. In addition, researches indicate that energy savings can enhance up to 76% by taking into account daylight and occupancy status [43].
In this section, both above mentioned approaches are considered to develop intelligent lighting system in order to minimize power consumption and provide sustainable lighting system. Economic analysis is required to be carried out to evaluate this new approach.
This integrated approach enables us to adjust brightness of lamps to a preset level, considering daylight coming into the room and also prevent unnecessary lighting in unoccupied places. In the economic analysis, LED lighting type is selected as its power consumption is the lowest among other types of light bulbs, and hence it is expected minimum energy cost saving to be achieved. In case, other type of bulb is used, such as fluorescent or incandescent bulb, the energy saving due to using this intelligent lighting system shall be much higher.
Energy consumption can be reduced significantly when light bulb’s output is controlled automatically. Two methods are commonly used for lighting control. First method uses individual lighting control system in which each light bulb’s output is adjusted independently according to light output level of its neighbor bulbs, the second method is networked lighting control system, which is more effective than the first method because all bulbs communicate intelligently with each other in order to achieve the required level for the room light intensity.
Networked lighting control system can be classified as DLCS (distributed lighting control system) for first method, or CLCS (centralized lighting control system) for second method. in DLC systems, each light bulb’s sensing data is received by the controller, and they can communicate with neighbors in order to adjust their output level according to each other’s state. However, in central unit CLCS which receives the status of each node based on information obtained from the sensors, and then performs control actions via actuators. In this system, central unit determines the output level of each light bulb on the basis of data obtained from sensors. In CLCS, many tasks are performed by central unit, such as, acquiring sensors’ data from each node, estimating the optimal state where each light bulb will meet light requirements of the room (Figure 8).
C LC system and DLC system.
PIR (Passive infrared) sensor is used to sense occupancy in places. PIR sensor detects occupancy at places and send commands to the controller to switch on or off lights. Light intensity sensor(s) is used to give the controller the required data. The control unit sends signal to light dimmer(s) to control the LED light imitation to achieve the preset Lux level required for the room considering daylight.
The term called intelligent luminaire is connected to a smarter level of illumination where devices are capable of creating lighting comfort, energy efficiency, and easy controllability. The concept which is named intelligent lighting system corresponds to a system that communicates and cooperates with many luminaires, creating a node that satisfies user requirements. The key goal of this kind of system is to save energy and, at the same time user comfort by the means of network communication. In Figure 9 the block-diagram of intelligent lighting system is illustrated. It is assumed that lighting system is dimmable (controllable) in order to provide intelligent method to tune the Lux level to the present value determined by the controller.
Block diagram of intelligent lighting system.
Firstly, this system checks for occupancy. If there is no occupancy, Arduino controller sends commands to AC light dimmer (which is controlling the intensity of light bulbs) to switch off lights. If there is somebody in the room, PIR sensor detects occupancy inside the room and activate Arduino controller. Consequently, the controller sends signal to the dimmer(s) to switch on the light and tune the lux of the room to achieve the preset value based on the input provided by the light intensity sensor(s).
The intelligent lighting system contains PIR sensor, BH 1750 light sensor, Arduino Mega, AC light dimmer, LED and light bulb.
PIR sensor is used to detect occupancy in the room. Light sensor is used to measure the amount of light in lux. Arduino Mega is used as a controller. Ac light dimmer is used in order to adjust the brightness of LED bulb. To monitor the amount of light (PV) and set point (SP), LCD is used. LED bulb is used to provide illumination in the room.
PIR sensor is one of the simplest and inexpensive type of occupancy sensors and this type of sensor is widely used around the world. It is capable of measuring various air temperatures in the room. When there is somebody in the room, sensor sends a signal to turn on or off lights. When object is moved in the sensor’s field of view, infrared lights which is radiating from the objects are measured by PIR sensor. People have a temperature that is higher than perfect zero and thermal energy is emitted from people in the form of radiation. During the day, the wavelength of radiation is approximately 9–10 micrometers. PIR sensor has capability to detect the wavelength of radiation which only arise when a person comes to sensor’s field of view. The radiation emitted by all objects which has temperature above absolute zero cannot be seen by human eye, since it is emitted at infrared wavelengths, however, electronic devices, such as PIR sensor, can detect it. This kind of sensors works totally by sensing the energy emitted by objects. When the amount of heat varies in intensity or position, sensor activates the controller.
PIR sensor which is used in this Intelligent Lighting System possesses pyro-electric sensor module that is designed for the detection of human body. This sensor has sensing range from 3 m to 4 m, and lens angle is about 140 degrees [44]. One of the advantages of PIR sensor compared with other types of occupancy sensor is that it is not complex, effortless to install, and it has compact size which is 28*28 mm. In addition to this, it is highly sensitive, power consumption is very low, and can perform under temperature from −15 to 70 degree. Most significantly, as contrasted with other sensors, it can penetrate walls in which motion can be anticipated and it is cheaper compared with other sensors. However, a constant and slight motion cannot be detected by PIR sensor and this sensor is sensitive to temperature. Another negative side of this sensor is that its field of view is smaller than other type of occupancy sensors. Moreover, this sensor cannot be mounted near the places where temperature changes commonly. But for application of indoor industrial building, this senior is adequate to be used.
BH1750 sensor is used in order to measure light intensity inside the room. This is a digital light sensor and it is used in the majority of mobile phones in order to adjust screen brightness, depending on lights coming from outside. This sensor has capability to measure directly lux value and there is no need to convert measured value to lux. This sensor uses I2C protocol to communicate with the controller. This protocol makes it easy to use with microcontroller. SCL and SDA pins which sensor have are required for I2C protocol. One of the advantages is that there is no need for calculation because we can get directly lux value by the means of this sensor. This sensor measures light intensity based on the amount of light which is hitting on it. The voltage between 2.4 V and 3.6 V and 0.12 mA current is needed to operate this sensor. The main component of BH1750 sensor is illustrated in Figure 10.
BH 1750 sensor circuit.
Arduino Mega is used as a master to control all slaves. It is the brain of this Intelligent Lighting System. It is a type of microcontroller board and uses ATmega 2560 microcontroller. Arduino Mega has 70 I/O pins. Fiftyfour (54) pins of Arduino Mega are digital I/O pin and 14 of them can be used as PWM pin. Other 16 pins are analog I/O. In addition to this, it consists of 4 UARTs, 16Mhz crystal oscillator, USB connection, power jack, ICSP header, and reset button. Arduino Mega can simply be connected to the computer and programmed. There are many types of shields used for several purposes can be added to the Arduino mega [13].
LED light bulbs are the best choice to use in energy saving lighting systems and they have great advantages over the fluorescent lamps and incandescent light bulbs. In these days, LED bulb technology has developed and this technology offer light bulbs which can be used for many applications. In addition to this, this type of light bulbs offer dimmable and non-dimmable options and it creates opportunity to be used in intelligent lighting systems. LED bulbs are very durable and no mercury is used in this type of bulbs. Although the initial cost of LED bulbs is higher than other types of bulbs, they are cheaper to use for overall life of the light bulb compared with fluorescent or incandescent light bulbs. For all of these reasons, it can be beneficial to use led bulbs instead of other types of bulbs in the Intelligent Lighting Systems [44].
AC Light Dimmer is used to adjust the light intensity by dimming the light bulb [45]. There are various methods for dimming, the usual way is to use variable resistor which change the voltage coming into the lamp. Nevertheless, when variable resistance is used in order to change the brightness of lamp, resistance converts some part of energy into the heat that is not used. An effective method for dimming is to turn off AC power regularly and provide only some portion of full wave to the light. It could sound strange at first, because it will produce flicker, however it is not visible by human eye, if the periodic light switches and phase of AC power are locked. In order to accomplish the dimming, two circuits are required, zero-crossing detector and pulse-controlled switch, respectively. This is used in order to maintain switching with the power source in phase. And, to deal with 220 V AC, safety precautions should be implemented. That is why, circuit should be mechanically and electrically isolated from outside by the means of metal box and optoisolators, accordingly. The zero-crossing detector is a full wave rectifier with high power resistors that is used to reduce voltage (Figure 11). And, the pulse-controlled switch contains a Diac or Triac.
Pulse control using AC light dimmer.
The response of system will be illustrated for three different preset values and three background in the room. The response of the system will be represented for occupied conditions. In unoccupied conditions, the intensity of light bulb will be set automatically to zero lux. In Figure 12, the response of the system is illustrated for preset value of 75 lux and external daylight with the amount of 25, 50, and 75 lux, ascending and descending. Another case is considered in Figure 13 represents the response of the system for setpoint of 150 lux and additional daylight with the amount of 50,100, and 150 lux, ascending and descending. And last test case is considered in Figure 14 shows the response of the system for setpoint 300 lux and external daylight with the amount of 100, 200, and 300 lux, ascending and descending. It is obvious from the results, the dimmer adjusts the light intensity of light bulb to achieve successfully to the present value, considering the external light coming into the room.
The response of system for 75 lux SP.
The response of system for 150 lux SP.
The response of system for 300 lux SP.
The transient state of the system response is not described in these graphs, only steady state is taken into account, since human’s eye does not recognize to the fast changes happen in the amount of light. Moreover, in general, the rate of the change in the daylight occurs slowly and gradually, consequently, the response of the controller will change the intensity of the light emitted from the controlled lighting system in small steps which are comfortable for the eye. Hence, the transient state is not concern for the proposed intelligent lighting system.
In this section, Techno-Economical evaluation is discussed that includes direct and indirect benefits obtained from using the proposed intelligent lighting system. As mentioned earlier in this Chapter, Direct benefits are categorized in two parts; operational and maintenance cost. However indirect benefit is categorized also into two parts, introducing more oil/gas sale opportunity and reduction of pollution. And, the cost of this intelligent lighting system is negligible compared with other lighting systems [46, 47].
Direct benefits of the proposed Intelligent Lighting System are explained as following:
This section determines the energy gains that intelligent lighting system can provide during the day. In order to achieve this, the response of controller is assumed to be maintained during the day. By considering occupancy status and level of illuminance during the day, energy savings which intelligent lighting system can provide may be calculated. Survey [37] illustrates that workers’ illuminance preference is approximately 300 lux, and energy waste is generated by over-illumination and turning on lights in unoccupied places.
In Figure 15, Data of illuminance and occupancy status during the day and workers’ illuminance preference in typical open-office are illustrated. In this survey, it is assumed that approximately 60% of daylight is coming into the room. From Figure 15, it can be observed that workers arrive at office at approximately 9:00 AM, occupies the working area and turn on the lighting system, because the level of illuminance is less than 300 lux (However, lighting system plus daylight coming into the room provides more than 300 lux). Thus, at the end of working hour, the lighting system was switched off about at 19:00. Also, from It can also be observed that workers leave working area at different times of the day, but lighting system turned on by causing the energy waste. In addition, between 15:00 and 17:00 the illumination which is generated by daylight is sufficient to satisfy the illuminance requirement at the office and lighting system is however switched on by causing over-illumination.
Data of illuminance and occupancy status in typical open-office.
This data represents that thanks to daylight utilization technique, energy can be saved significantly between 9:00 and 19:00 by controlling the amount of light provided by the lighting system. In addition to this, occupancy sensor will contribute us to save energy by switching off lighting system when there is no occupancy in the working area. Finally, the energy savings can be calculated from the Figure 15 by comparing the Areas under the curves. In order to find the energy savings, the area of curves, which are generated by the outputs of intelligent lighting system and Setpoint, should be calculated between 9:00 and 19:00. And, using the following equation, the percentage of energy savings accomplished from intelligent lighting system can be estimated.
From the equation above, it is calculated that in typical open-office energy savings can be approximately 81.7% by implementing proposed intelligent lighting system.
In Figure 15, it is clearly seen that operation hours of light bulbs reduce from 10 hours to approximately to 5.5 hours. So, implementation of proposed intelligent lighting system contributes also to reduce maintenance cost. The life span of light bulbs increases significantly, since lights are switched on at certain times of the day. From Figure 15, percentage of reduction of maintenance cost can be calculated by the means of following equation.
From the equation above, it is calculated that in typical open-office, maintenance cost can be reduced about 45% by implementing proposed intelligent lighting system.
Explanation of indirect benefits will be given in detail in the following paragraphs.
First benefit is that country can export larger amount of gas, since the consumption of gas will be reduced due to the energy savings caused by proposed intelligent lighting system. By using the selling price of $4.618/MMBtu on the basis of US Energy Information Administration Henry Hub/NYMEX, natural gas valued futures prices. Considering 1% annual escalation factor, equivalent energy rate of 5.6¢/kWhr used to measure the energy generated for one year. And, sales opportunity for the natural gas can be estimated annually by the means of equation Eq.(1) that can be used to calculate the annual gas sale opportunity for any project using this Intelligent Lighting System.
Second indirect benefit is that pollution caused by power plants can be reduced significantly. When the amount of power consumed is reduced, the amount of toxic fumes released by power plants will be reduced. The majority of power plants burn crude oil, coal, fossil fuel etc. Hence, this causes the emission of carbon dioxide that accounts for the majority of pollution. Carbon dioxide is released into the air and causes the absorption of sun’s warmth and heat in our atmosphere. When power plants burn more fuel in order to generate more energy, extra carbon waste traps cause too much heat. When carbon dioxide emission is reduced, it will cause less pollution. Eq. (2) can be used to calculate the Annual Saving in Pollution that can be gained in any project using this Intelligent Lighting System.
Nowadays, energy saving is one of the big problems, that is why energy-efficient lighting systems proceed rapidly over the past ten years [43]. Led light bulbs are the best choice to use in energy saving lighting systems and they have great advantages over the fluorescent lamps and incandescent light bulbs. In these days, led bulb technology has developed and this technology offer light bulbs which can be used for many applications. In addition to this, this type of light bulbs offers dimmable and non-dimmable options and it creates opportunity to be used in intelligent lighting systems. Led bulbs are very durable and no mercury is used in this type of bulbs. Although the initial cost of Led bulbs is higher than other types of bulbs, they are cheaper to use for overall life of the light bulb compared with fluorescent or incandescent light bulbs. For all of these reasons, it can be beneficial to use led bulbs instead of other types of bulbs in lighting systems.
One of the bunch of LED bulbs is diffused LED bulbs. It is covered by lens which have dimple shape, and this shape support to spread light around a big area. Nowadays, because of their tremendous efficiency, people increasingly use this type of bulbs. This type of bulbs is available in standard Edison bases, and they can be used for a lot of purposes, such as, reading lamp, lighting for rooms and offices, and some other applications in which light can remain on for a long time.
Flame Tip, Candelabra Base LED bulbs is another type of LED bulbs and it is used in many applications. The purpose of designing such type of light bulbs is to take the place of incandescent candelabra bulbs. This type of light bulbs is significantly effective because they can deliver corresponding light of 25 to 35 W and light does not spread top to bottom as far as typical lights, because of heat sink.
LED Tube Light bulbs is another type of LED light bulbs and it is used in a lot of applications. The purpose of designing this type of light bulbs is to replace typical fluorescent tube lights. They exist in 8 and 16 W. In commercial sites, fluorescent lights are frequently installed in high ceilings and using Led Tube Lights instead of fluorescent tube lights is extra saving, because the frequency of replacing bulbs is significantly decreased.
The life of LED bulbs is approximately 10 times more than incandescent and fluorescent light bulbs. The main reason why they are more effective is that they do not have filament and they are not destroyed under conditions in which typical incandescent and fluorescent light bulb can be damaged. This type of bulbs does not cause any heat, but common incandescent lamps heat and help to increase the room temperature. LEDs avoid this problem and contribute to reduce the air conditioning cost. In the manufacturing process of LED bulbs, no mercury is utilized and this kind of bulbs use approximately 2–17 W electricity. LED bulbs reduce electricity cost, remain cool and avoid the replacement cost, because they have long life.
Although, initial cost of LED bulbs is higher, this cost compensates over time in electricity saving. The use of LED bulbs commercially adopted, because maintenance and replacement cost was significantly higher. Maintenance and replacement cost in LED bulbs are considerably less compared with others and the initial cost of LED bulbs is continuing to decrease.
Consider standard office with dimension 3mx4m. As per table Table 12, the design lux level is 500 lux. Using matrix distribution 2x2 with 60cmx60cm light fitting, each consists of 4 lighting tube Fluorescent (25 W) or LED (9 W), the office Traditional lighting load shall be 400 W or 144 W respectively. For 9 hours working duty, the annual consumption shall be 4730kWh and 1314kWh.
Applying Equation-11, the office annual energy consumption can be reduced to 865.59kWh and 240.462kWh for Fluorescent lighting and LED lighting respectively.
From Table 13, cost of electricity (0.10per KWh). Accordingly, from two the values, 240.462kWh and 865.59kWh, Annual Energy Saving Norm/Office for offices using Fluorescent lighting and LED lighting can be calculated to be 24$/office and 86.6$/office respectively. For example, if this technique applied on 100 Administration Building with 50 room each, so the total Annual Saving can be 120,000$ and 433,000$ for Fluorescent lighting and LED lighting consequently. This example gives good impression how much reasonable saving can be obtained by applying such technique in industrial buildings.
Conditions | Led Bulbs | CFL | Incandescent |
---|---|---|---|
Light bulb projected Lifespan | 50,000 h | 10,000 h | 1200 h |
Watts for per bulb | 10 | 14 | 60 |
Cost for per bulb | $35.95 | $3.95 | $1.25 |
KWh of electricity used over 50,000 hours | 300–500 | 700 | 3000 |
Cost of electricity (0.10per KWh) | $50 | $70 | $300 |
Bulbs needed for 50 k hours of use | 1 | 5 | 42 |
Equivalent 50 k hours bulb expense | $35.95 | $19.75 | $52.50 |
Total cost for 50 k hours | $85.75 | $89.75 | $352.50 |
Economic comparison between LED, CFL and incandescent bulbs.
To conclude this section, it can be highlighted that most places are over illuminated because background light is not considered in the design sage. In addition, light is switched on in unoccupied places which causes waste of energy. Therefore, Intellect Lighting System is very essential to overcome this problem to control indoor lighting intensity taking into account occupancy status and background light coming into the room in order to adjust level of illuminance in efficient way. As a result, it is worth to highlighted that Intelligent Lighting System uses properly selected LED bulbs not only reduces power consumption, but also reduces maintenance cost, pollution caused by power plants and increases opportunity for gas sales. Finally, typical Annual Energy Saving Norm (Energy Saving$/Office) is calculated for both cases, offices using Fluorescent lighting and LED lighting.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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