Perovskite materials used as photocatalysts (ABO3, AAIBO3, AAIBO3, ABBIO3, AB(ON)3, and AAIBBIIO3) for degradation of pollutants.
\r\n\t
\r\n\tIn this book Advanced application of radionuclides are introduced. New global trends on safe application of radionuclides in human life is elucidated.
Current implementation of new technologies for Communication, Navigation, Surveillance and Air Traffic Management (CNS/ATM systems) along with computational improvements on airborne and ground systems developed in the last two decades, point out the need for more strategic navigation and air-traffic control procedures based on four-dimensional (position plus time) trajectories. Moreover, the CNS/ATM infrastructure will help to achieve more shared real-time information among aircraft, airlines and air-traffic services providers (i.e. Air Traffic Control –ATC- providers, meteorological information providers and air space resources information providers). Then, general requirements for a next-generation of Air Traffic Management (ATM) system is that entities must share real-time information about aircraft state and intentions, air-space state and resources, meteorological conditions and forecast, etc. From this information coordinated and strategic actions should be carried out by them in order to achieve efficient and free of conflict 4D trajectories.
Several systems have been used on last years to help controllers and pilots to take decisions in a more strategic way. Some examples of these systems are OASIS [1], MAESTRO [2], COMPAS [3], CTAS, [4], etc. In addition, in the PHARE program [5], on-board trajectory predictions were used to inform the air traffic controllers about aircraft intentions. Results of the previous proposal showed potential advantages of a more strategic navigation and Air Traffic Control (ATC) based on 4D trajectories.
However new efforts are still necessary for achieving an ATM system characterized by a greater aircraft autonomy to select an optimal flight path and by a higher automation of air-ground coordination tasks. Thus, the Free Flight operational concept [6] suggests a future ATM where the aircraft are only restricted by global goals that must ensure safe and efficient air traffic flows. By other hand, the Trajectory Based Operations (TBO) [7] attempts to give more specific to the free flight proposal. In this case TBO concept proposes that coordination activities between ATM system elements are intended to achieve efficient and free of conflict 4D trajectories. In a hypothetical TBO scenario, aircraft will calculate their User Preference Trajectories (UPTs) taking into account air-space constraints. In addition, an ATC with the role of a central agent should drive air ground negotiation processes in order to provide free of conflict trajectories. Furthermore, under certain circumstances aircraft self-separation could be possible and air-air negotiation processes could be only supervised by the ATC. After a negotiation is performed the aircraft must fly the negotiated trajectories until a new set of trajectories were negotiated. Meanwhile, if emergence or contingence arises, a new negotiation process could be triggered.
In summary the new operational concepts propose:
Four dimensional trajectory based operations defined by the aircraft position and time. These trajectories must suit the preferences of the aircraft while preserving the efficiency and safety of surrounding air traffic flow.
Accessibility and distribution of updated data among all entities involved in flight operations.
A more distributed reallocation of the roles of aircraft and services of air traffic control to achieve their respective goals, in contrast with the current scheme of responsibilities characterized by a ground-centralized monitoring and air traffic separation activities.
Application of these operational concepts by 2020+ is the key target of current research initiatives such as SESAR (Single European Sky ATM Research) and Next-Gen (Next Generation Air Transportation System) [8-9]. These research programs show that the set of activities aimed to validate procedures and technologies in order to implement the cited paradigm is diverse and extensive. To identify the interdependence of such activities, we propose a framework that classifies them according to a sequential time process (see Fig. 1). The first two groups of activities are referred to the analysis of the potential of CNS/ATM systems and as well as feasible operational concepts: i.e. Free Flight, TBO, etc. Proposals of operational concepts consist of generic specifications that requires of concrete procedures for conducting operations navigation and air-traffic control. Parallel to procedures design, on-board and ground systems and underlying mathematical models have to be also developed. Then, initial design of procedures and their associated systems can be considered as an iterative process that needs to be validated by means of analytical simulation. This process is a key issue as a previous stage to the Human-In-The-Loop (HITL) simulations and flight tests. HITL simulations and flight trials allows defining specific standards (e.g Procedures for Air Navigation Services –PANS- or for Required Performance Navigation –RNP-) for the actual implementation of the operational concept.
Several attempts for developing simulation and design analysis tools have been proposed [10-16]. Results of these studies show that it will be necessary more detailed and structured conceptual models to give support to analytical simulation tools to address the paradigm shift in the ATM procedures. Moreover, the close interdependence between coordination procedures, systems to execute them and their underlying mathematical models must be clearly set out in the conceptual model.
Research and development activities for new operational concepts
Fortunately, current state of the art of Multi-agent technologies provides methodological approaches and tools to develop such models. Then, the main goal of this chapter consists in illustrating how recent proposals in agent-oriented methodologies are a suitable approach for analysing and modelling new air traffic scenarios in order to obtain such conceptual models.
The chapter begins summarizing main contributions on modelling and simulations of future ATM. This review shows that new simulation platforms should be based on more detailed and structured conceptual models. In additions, it suggests benefices of multi-agent methodologies for approaching this problem. In section 3, multi-agent approaches for modelling several aspects of cited ATM are analysed. After a brief description of particularities of the multi-agent theory, a review of its most recent applications within the air traffic scope is presented. This exploration highlights the need of taking advantage of modern multi-agent technologies for developing robust and modular conceptual models of the new ATM. Section 4 presents the multi-agent methodologies as useful tools for analysing and modelling this ATM in order to facilitate the design and take key decisions for the implementation of new procedures. From them, one of the most recent agent methodologies, named Prometheus, has been selected to integrate the system specifications, inter-agent coordination protocols and agent inner processes as components of an ATM conceptual model, described in section 5. In order to simplify the applicability of the mentioned methodological approach, it has been applied for analysing and modelling a particular air traffic scenario of arrival air traffic operations. In this case we will consider this particular ATM model as an Air Traffic System (ATS). Moreover the model is focused on the inner architecture of an ATC System. Although these simplifications, the obtained model under this methodological approach can be extended for adding new gate-to-gate air traffic scenarios, coordination procedures as well as improved versions of the technical support to execute mentioned procedures. Section 6 resents guidelines for a software implementation and results of an illustrative example of current implementation state. Finally, conclusions are presented in section 7.
The simulation of air traffic scenarios with different levels of fidelity is the mainstay of the methodology used widely by the scientific community to develop new operational concepts [8-9]. Real-time simulations are, in general, HITL simulations intended for human factors evaluation while navigation and/or traffic control procedures are performed. Fast-time simulations are centred on the analysis of several issues no specifically focused on human factors: mathematical models, algorithms, negotiation and/or decision-making processes, quality of service measures, etc. Obviously real-time simulation platforms are able to carry out fast-time simulations by including computer models for performing tasks assigned to the human element.
Some of the multi-aircraft simulators currently used for air traffic research purposes are: NLR\'s Air Traffic Control Research Simulator (NARSIM)) [10], Pseudo Aircraft System (PAS) [11], Target Generation Facility (TGF [12], ATC Interactive for the future of air traffic control[13] and Multi-aircraft Control System (MACS) [14]. Three main consequences can be derived from the analysis of previous works. First, more detailed proposals are necessary for supporting automated air-ground coordination processes. Second, modelling such automatic coordination procedures system requires a parallel specification of their associated systems (i.e. user interfaces, mathematical models and algorithm for making decisions, etc). Third, new conceptual models and simulation frameworks require a high modularity and scalability for making possible the progressive incorporation of new or modified procedures and their associated systems as they are designed or evaluated.
In the context of modelling such complex distributed systems, Multi-Agent Systems (MAS) theory gives natural solutions [17-18]. This theory provides a suitable framework to analyse and model the organization of a set of autonomous ATM entities that coordinate and negotiate their actions in order to achieve their respective goals.
The dynamic nature of air traffic and its geographical and functional distribution have attracted the attention of agent researchers since the last decade. For example, Optimal Aircraft Sequencing using Intelligent Scheduling (OASIS) [1] is a system used at the airport of Sydney to help air traffic controllers on arrival and approach air traffic sequencing operations. However, most of agent-based contributions are oriented to developing very basic aspects of the ATS system. The proposals can be classified into three categories:
Analysis of negotiation patterns between agents in free flight air traffic scenarios. Within this category, Wangerman and Stengel analyse the dynamic behaviour of aircraft in the airspace as an intelligent system (Intelligent Aircraft/ Airspace System or IAAS) [19-21]. This system consists of three types of agents, airlines, aircraft and traffic managers, and the dynamic behaviour of the agents of IAAS is analysed from the perspective of a distributed approach. In this context, the principles of negotiations are a way to implement distributed iterative optimization of IAAS operations. In [22] Harpert et al. propose an agent-oriented model of an ATM in a free flight and a model of distributed decision making for the resolution of conflicts in the ATM. This proposal includes a distributed optimization scheme in which the agents generate and evaluate proposals options that best suit their preferences on a utility function and a multi-attribute decision tree scheme. Besides, the declarative capabilities of an intelligent agent can be modelled by expert systems [23-25]. To set the ground rules of the expert system, tasks were classified in [21] into four groups: emergency tasks, tasks of a specific mode of operation, negotiation tasks and routine tasks.
Avionics systems for autonomous operations in distributed air traffic management scenarios. Works in this category propose designing avionics systems based on multi-agent systems. The proposed developments basically consist of systems for automatic conflict resolution, automatic warnings and recommendations to the crew [26-28]. In [26] authors propose a design of intelligent traffic agent developed to detect and solve conflicts on board in a free flight environment. An extension of previous work presented in [27] proposes a design of an executive agent that resolves conflicts for both the traffic and bad weather areas. In this case an agent’s hierarchical architecture is suggested for making decisions based on the information produced by a traffic agent proposed by [26] and a weather agent. Finally, in [29] capabilities required of future flight management systems (FMS) in the cabin were characterized by means of agent-oriented analysis of the air navigation and arrival operations into a distributed environment. This analysis was later extended to define capabilities required to carry out automated arrivals and departures at uncontrolled airports [30].
Simulation systems for the analysis of advanced air traffic concepts. This group includes several simulation platforms used for the design and validation of procedures and systems proposed for next generation of air traffic scenarios. In [15, 17] a multi-aircraft control platform is proposed to increase the realism and flexibility of HITL simulations. Functional descriptions of pilot and ATC perspectives within this platform are presented in [15]. In [17] the ATC agent model is analysed in order to identify its roles and responsibilities in future ATM systems. Another development of functional architecture airspace for an Airspace Concept Evaluation System (ACES) is presented in [16]. In a later work an agent-oriented model of the CNS/ATM infrastructure of ACES was proposed in [31]. Finally, in [32] a design of an experimental air traffic simulator implemented as a Java environment SMA is presented.
The proposals based on multi-agent systems presented above cover a wide spectrum of issues related to air traffic operations in a free flight environment. However it is clear that effective implementation of the new operational concepts involved in the future scenario still requires more structured models that take into account the tight relationship between procedures and systems to support them. In addition, as it was explained before, the model should be scalable enough to allow a progressive integration of the following elements into the model:
Operating procedures for ATC and aircraft, specifying: (i) roles and tasks assigned to ATC and flight crew and (ii) coordination rules and negotiation protocols among the involved agents
ATC and on-board functionalities to help to execute such procedures at several automation levels.
Underlying mathematical models and algorithms to give support to previous functionalities.
High level languages that allow for accurate intercommunication between aircraft and ground systems.
Fortunately, methodologies for developing multi-agent systems have reached a noticeable degree of maturity in recent years, becoming an invaluable tool to achieve a comprehensive analysis and modelling of complex scenarios. Thus, the analysis and modeling of interactions in terms of coordination and negotiation strategies between agents provides useful guideline for developing new schemes for developing automated ATC and navigation procedures. In turn, the study of the agents’ behaviour and their internal architecture for the mentioned coordination processes provides a more precise identification of the functionalities required by on-board and ground systems to execute mentioned these procedures.
Agent methodologies provide with a set of guidelines to facilitate the development of multi-agent systems over several stages since the initial draft of idea until the final detailed design. In this way, current multi-agent technology provides practical and formal methodologies to analyse and design, in a structured and consistent manner, the following issues: (i) roles and functionalities of autonomous entities (agents) that take part in an operational scenario, (ii) interactions between agents (or agent protocols) and (iii) inner architecture and dynamic behaviour (processes) of agents. Besides several agent platforms have been proposed as middleware tools for translation the conceptual model into an executable model.
The design of MAS requires not only new models but also the identification of the software abstractions, since this paradigm introduces a higher abstraction level when compared to traditional approaches. They may be used by software developers to more naturally understand, model and develop an important class of complex distributed systems. The key aspects of problems being modelled under a MAS methodology are: establishing a set of coarse-grained computational systems (agents) and interaction mechanisms for a goal to obtain in the system that maximizes some global quality measure, assuming a certain organizational structure which can be assumed to keep fixed (agents have certain roles and abilities that do not change in time).
Some of the main agent-oriented methodologies are MASCommonKADS [33], Tropos [34], Zeus [35], MaSE [36], GAIA [37], INGENIAS [38]. In [30-41] a comparative analysis of the main methodologies is presented. MAS-CommonKADS is a methodology for knowledge-based system that defines different models (agent model, task model, organizational model etc.) in the system life-cycle using oriented-object techniques and protocol engineering techniques. Tropos is a requirement-based methodology; Zeus provides an agent platform which facilitates the rapid development of collaborative agent applications; MaSE is an object-oriented methodology that considers agents as objects with capabilities of coordination and conversation with automatic generation of code and Unified Modelling Language (UML) notation; Gaia is intended to go systematically from a statement of requirements to a design sufficiently detailed for implementation; and INGENIAS proposes a language for multi-agent system specification and its integration in the lifecycle, as well as it provides a collection of tools for modelling, verifying and generate agents’ code.
A descriptive analysis these methodologies are beyond of these chapter goals. However after a previous study we have selected Prometheus methodology as the most suitable one. Prometheus agent-oriented well-established methodology has been selected to provide guidelines to develop the mentioned multi-agent system [42]. We argue that Prometheus suits well for solving our problem due to: (i) the highly detailed guidelines for defining the initial system specification, (ii) the modularity of the agent’s internal architecture around the concept of capability (providing a direct correspondence between capabilities and functionalities of airborne and ground systems), (iii) the easy translation from the conceptual model into an executable model by means of current agent platforms that provides software infrastructure as it will be explained later on.
Prometheus methodology covers the entire process of design and implementation of intelligent agents. It includes three phases (see Figure 2): system specification, architecture design and detailed design [42].
System Specification stage defines the objectives or goals of the system. Goals help to identify functionality required to achieve them, as well as a description of the interface between the system and its environment in terms of inputs (Perceptions) and outputs (actions) of the system. The identification and refinement of the objectives are carried out, in an iterative manner, from the definition of different use case scenarios. Scenarios illustrate the operation mode of said system. The concept of scenario (or use case scenario) comes from the object-oriented software methodologies, but with a slightly adapted structure that provides a more integrated than the mere analysis of the isolated system.
Later on, in the Architecture Design phase, the description of the system structure is represented by means of diagrams that identify the agents of the systems and their interactions in terms of communication messages and protocols. Protocols represent specific communication schemes. They can be modelled using Agent Unified Modelling Language (AUML) that describes the interactions of agents in different scenarios of use cases.
Finally, the Detailed Design phase consists of designing internal processes carried out by each agent and an inner architecture that describes how these processes are organized and implemented. Prometheus proposes implementing agent tasks by means a set of different plans that are triggered when determinate events occurs. Plans are grouped into several groups associated to the execution of specific tasks. A group of plans as well data used or produced by them constitutes an agent capability. Then focus of this stage is to define capabilities, internal events, plans and detailed data structures. In this way capabilities are modules within the agent that use or provide related data types. Capabilities can be nested within other skills so that in the detailed design, the agent will have an arbitrary number of layers in an understandable complexity at each level. Thus, capabilities can be constituted by other sub-capacities or, at lowest level, by plans, events and data. The plans set out the set of tasks performed to achieve a particular purpose. They are also triggered by certain events (internal or external messages, perceptions, etc.). As a result of this stage are general diagrams of each of the agents (which show higher level capabilities of the agent), charts of capabilities, descriptors detailed plans and data descriptors.
Prometheus methodology phases [42]
Moreover, the tool Prometheus Design Tool (PDT) facilitates the tasks of the developer over the previous stages to provide information about possible inconsistencies in the design [43]. In addition, several software tools have been developed in recent years for software implementation of system multi-agents. One of the most extended is the Java Agent DEvelopment Framework\n\t\t\t\t(JADE) Platform [44]. JADE simplifies the implementation of multi-agent systems through a middleware that provides several resources through a set of library classes aimed to:
Implement agent’s tasks into a several JAVA classes named behaviours.
Provide agent intercommunication that complies with the FIPA (Foundation for Intelligent physical Agents) specifications [45].
Supply services for create and manage cycle of live of agents as well are their services into the multi-agent system.
JADE behaviours can be classified onto simple behaviours and composite behaviours. In turn, simple behaviours can be classified as:
One-shot behaviour, an atomic task to be carried out once, used here for initialization tasks;
Cyclic behaviour, which is iterated while exists, such as messages listening and processing;
Waker behaviour, or a one-shot behaviour invoked after a certain time; and
Ticker behaviour or a cyclic behaviour which performs a series of instructions executed keeping a certain fixed time, used in the platform for simulation numeric computation and graphical output.
Composite behaviours are three:
FSMBehaviour that consists of a class that allow defining a Finite State Machine by means sub-behaviours, where each of them represents an machine state
SequentialBehaviour that executes its sub-behaviours in a sequential way, and
ParalellBehaviour that executes their sub-behaviours concurrently and ends when a certain condition is satisfied (for one, several or all of them). In this way, agents are able to concurrently to carry out different tasks and to keep simultaneous conversations.
JADE Behaviours
As explained in the previous section, Prometheus methodology carries out an iterative process on three phases: specification system, architecture design and detailed design. Each of these phases provides guidelines for designing several model components. These components produce a hierarchical structuring mechanism which makes possible a model description at multiple levels of abstraction [19]. In addition, the structured nature of design components facilitates crosschecking for completeness and consistency of the model in each design phase.
In this phase, goals of our ATS model are identified. In turns goals are captured from a set of scenarios that illustrate essential aspects of the system operation. Scenarios and goals help to recognize initial system functionalities and to examine the system-environment interface in terms of inputs (Perceptions) and outputs (actions) [45]. Thus, scenarios are use cases that contain a sequence of steps each of them relating to a goal, an action, a perception or another scenario.
For outlining the mentioned scenarios, a generic automated air traffic scenario was considered as a distributed process where several autonomous and proactive entities (agents) plan and execute a set of coordinated tasks to provide arrival and approach free of conflict 4D trajectory. This operational scenario is particularly critical in arrival air traffic operations due to the high variability of the speed, heading and altitudes that could affect to the degree of predictability of several converging trajectories. Moreover, guidelines from scenario proposed in DAG-TM (CE-11) project [46] have been taken into account. According to referred guidelines the flight crew: (i) could negotiate arrival preferred trajectories with the ATC; (ii) is responsible for maintaining longitudinal spacing between consecutive aircraft once a trajectory (o constraints) has been assigned.
In this operational scenario, the following agents have been identified: Aircraft, Air Traffic Control (ATC), Meteorological Service Provider (MPS), Airspace Resources Provider (ASP) and Airline Operational Control (AOC). In addition several ATC agents could be defined in order to coordinate arrival ATC tasks with the ATC en-route or departure ones. MSP, ASP and AOC agents’ functionalities have been used to define the information required by the ATC and aircraft agents as well as essential protocols to accomplish this information.
Use case scenarios have been selected and organized taking into account perspective that each agent have about the generic air traffic scenario. Then, five root scenarios have been defined: (i) Manage Aircraft, (ii) Manage ATC, (iii) Manage Airline Operational Control, (iv) Provide Airspace Resources (v) Provide Weather Information.
Tasks of each one of above scenarios have been grouped into new sub-scenarios and so on. Figure 4 depicts a list of the most significant sub-scenarios deployed from the previous one. In particular Manage navigation procedure scenario and Manage ATC scenario are developed until the lowest level. In addition, this scenario architecture shows that air-ground negotiation processes are contained into specific air-ground negotiation scenarios which are shared by Manage Aircraft and Manage ATC scenarios.
To illustrate how scenarios can be deployed we will focus on the Manage ATC scenario. This scenario contains the following four scenarios:
Update ATC environmental information scenario, which covers associated processes to collect information about status and intentions of aircraft, airspace resources (including restricted flight areas), weather conditions, etc.
Manage ATC procedures scenario. It includes the processes related to maintaining the separation of aircraft to achieve an efficient traffic flow.
Manage on board surveillance scenario. This scenario contains tasks for monitoring air traffic. These tasks are aimed at identifying aircraft trajectory deviations and potential conflicts with other aircraft or obstacles. It also provides viable solutions for correcting these anomalies and events for triggering specific processes in order to implement the solutions mentioned above.
Architecture of the main scenarios for Trajectory Based Operations
Manage contingences scenario that includes tasks for analysing air traffic contingencies and circumstances in which they occur (e.g. aircraft malfunctions, on-board contingences, etc.). It also includes decision-making processes to determine actions to be carried out regarding the management of traffic control procedures.
Focusing on the Manage ATC procedures scenario, the next three scenarios are deployed:
Set ATC traffic involves actions for receiving or transferring air traffic from or to other adjacent ATC agents.
Set strategic separation. This scenario contains tasks for planning aircraft trajectories and assigning them by means a negotiation process. Therefore it is a key scenario for modelling automated procedures for TBOs and it should contain several negotiations sub-scenarios.
Set tactical separation. This scenario contains tasks for modifying current flight trajectories when unforeseen contingencies arise. The tasks performed in this scenario are twofold: (i) to provide specific instructions for activating protocols aimed at aircraft separation in extreme situations of short-range conflicts and (ii) to authorize and supervise air-air negotiation for self-separation when separation responsibility has been delegated on the aircraft.
From the above scenario architecture a goals tree can be obtained. Lowest level goals help to identify functionalities and processes that the agent has to carry out to achieve them. For example, Figure 5 shows a particular set or goals that results from the ATC manage scenario. On it, the goal UPT air-ground negotiation consists of several sub-goals such as generate proposals for aircraft, evaluate proposals from aircraft or establish and an air-ground communication protocol for negotiate mentioned proposals and counterproposals. In addition, functionalities help to identify actions, perceps and data used or generated by the agents. Then, for the ATC agent the following perceptions can be identified:
Perceptions form external sensors: data from radar systems, WAN receivers, etc.
Perceptions from human-machine interface: menus and inputs options.
Actions: display traffic data and ATC procedure state data on screams.
In the architecture design phase the following aspects of the overall system are defined:
The system overview diagram. This diagram represents the static structure of the system, tying agents and main data used by them as well as their perceptions and actions. Furthermore communication interactions between agents are considered.
The set of interaction protocols that capture timing of communication of related messages between agents. These protocols are derived from the scenarios defined in the specification phase protocols and, therefore, they describe the system dynamic behaviour. Then, they have been depicted using an AUML notation [47].
ATC agent goals
Figure 6 shows a simplified representation of the system overview diagram. The simplification consists on representing only the main actions and perceptions of the ATC and aircraft agents as well as the main communication protocols. In a more complex system overview diagram, all these elements should be signed for all the agents.
After identifying the interaction protocols in the system overview diagram, protocols are designed. For the ATC and aircraft agents, protocols are aimed to: (i) improve agents’ knowledge base about the environment and/or the other agent’s intentions, (i) negotiate trajectories that could be in conflicts.
Simplified architecture overview
For a better understanding of automated air-ground coordination aspects, we will focus on describing a proposed air-ground negotiation protocol (see Figure 7). This protocol represents the core of the both the ATC strategic planning tasks and the aircraft navigation planning tasks. Although new scheme of negotiation can be defined from this design, all of them will use similar functionalities to evaluate proposals and generate counter-proposals. Therefore, this protocol and its associated functionalities provide guidelines and specification enough for developing new aircraft and ATC coordination procedures.
In Figure 7, the on board computation processes are represented on the left side of the aircraft agent lifeline. On the right side of the ATC agent lifeline we can observe computation performed by ground systems. Moreover, on the right side of this ground computation system, a new lifeline for other aircraft agents is showed.
Air-ground Negotiation Protocol
The proposed arrival negotiation protocol can be divided into two phases that are described next:
Phase 1: Before reaching Time Limit for Requesting Trajectory (TLRT)
In this phase aircraft calculates their preferred 4D Trajectory (Traj_0). To perform this computation, each aircraft agent uses the available information about meteorological conditions and arrival routes. This information is obtained by means of a previous communication procedure (no represented in Fig. 7) with the Meteorological Information Provider agent and the Air-space Resource Provider agent. Once the 4D trajectory is calculated, the aircraft requires clearance to the ATC to execute it. In this case, the TLRT represents a deadline time for requesting mentioned clearance.
The ATC agent receives Request messages from different aircraft that are periodically processed in-batch. After receiving these messages, the ATC evaluate if requested trajectories are free of conflicts. As a result, the ATC could confirm the trajectory initially preferred by the aircraft or it could propose new constrains for a new one (Traj_1). If the aircraft is agreed with previous information, it sends a corresponding message and the communication process finalizes. But if the aircraft wish to flight an alternative trajectory (i.e. a faster one), the negotiation protocols continues in a second phase.
Phase 2: Call For Proposal
In this phase, the aircraft makes a second counter-proposal in order to improve the previous ATC proposal, arguing reasons for it (for example certain operational contingences). These kinds of proposals (Traj_2) will be evaluated by the ATC. Those one that can be feasible will be accepted. In other case, the ATC will perform a new proposal (Traj_3) that the aircraft in turn can refuse or accept. When an aircraft refuses mentioned ATC proposal, it will have to select one that satisfy previous ATC constrains. But if the aircraft accepts cited ATC proposal, it will have to wait an ATC confirmation message before implement such proposal. This confirmation is necessary due to the ATC has to analyze air traffic state after receiving several aircraft messages accepting or refusing Traj_3 proposals. Then the protocol ends with aircraft messages informing about details of the last cleared and accepted trajectory.
Finally note that the software implementation of messages used in this protocol can be performed by a normalized FIPA support [45].
Finally, in the detailed design phase, the dynamic behaviour and the internal agent architecture are projected. The dynamic behaviour is described by a set of processes that agents carry out when they interact or make decisions. The internal agent architecture is represent by means an agent overview diagram that shows how these processes are organized.
Processes are represented by a flow diagram that links protocol messages with internal functionalities that evaluate and generate proposals. Notation used for depicting processes is showed in Figure 8. This notation is slightly different to the UML notation so that, instead of focusing on the activities it is focused on communications. Besides, we extend notation proposed by Prometheus methodology in order to include information about the different states of the air-ground negotiation protocol. These negotiating states are intended to enable automated negotiation processes whose evolution can be understood in supervisory tasks of pilots and controllers. Figure 9 shows the process carried out by the ATC while the air-ground negotiation protocol, previously presented, is in progress.
Agent processes like the described above can be implemented by means of plans. Then plans contain a set of instructions in order to: (i) carry out computations, (ii) take decisions (iii) generate or receive messages and new events. Moreover plans are to be triggered by specific events such as arrival messages or events generated by other plans.
The agent diagram overview consists of an agent architecture representation that indicates how all these plans are organized. Therefore, it shows interaction between plans, shared data and events. In addition Prometheus methodology proposes to organize plans that share similar functionalities and data into capabilities. Figure 10 represents Prometheus notation for representing elements of the agent overview diagram. Then, Figure 11 represents the ATC agent architecture diagram overview. On it, main capabilities of the ATC agent are depicted together with data used or produced, agent inner events and communication messages.
Notation used wihin agent processes
ATC Process Diagram for Air-Ground Negotiation Protocols
Prometheus notatino used in agent and capability overview diagrams
ATC agent architecture
Taking into account scenarios, functionalities, processes, events and data established in previous phases, plans has been grouped into the following capabilities:
Manage ATC Environment Information: This capability is associated with the goal of maintaining an updated on-board environmental knowledge. Plans for this capability capture information about weather forecast, restricted areas, air space recourses (e.g. available arrival routes and gateways), air space contingency events concerning to significant environmental changes and aircraft contingence events.
Traffic Monitoring: This capability checks the state and intentions of the aircraft according to air-ground agreements previously negotiated. In case of divergences, an air-traffic contingence event is generated informing about it.
Traffic Conflict Detection-Resolution: As its name suggests, it is responsible for detecting conflicts with other aircraft or obstacles (terrain, adverse weather areas, etc.). It also provides a set of ranked proposals for conflict resolutions. Furthermore, proposals are negotiated and/or implemented by means of other capabilities. To achieve above goals, plans of this capability are grouped into two sub-capabilities: (i) Conflict Detection Capability and (ii) Initial Conflict Solution Capability.
Conflict Detection Capability contains plans to implement algorithms for conflict detection. Therefore, it can be constituted by several plans each of them contains a specific model to detect short, medium and long term conflicts. Plan inputs are data about predicted trajectory, restricted areas, surrounding traffic state and intentions. Plans of this capability are triggered by events generated by plans of other capabilities that perform surveillance tasks as well as testing tasks within the trajectory planning processes. Conflict data calculated by previous plans are used by a specific plan to obtain a detailed conflict description and to generate conflict events.
Initial Conflict Solution Capability uses several inner plans to supply solutions according conflict data input. Results of these plans are used by other ones that generate conflict contingence events. Then events also contain associated information about feasible conflict solutions.
Manage ATC Contingency. This capability deal with deciding which kind of ATC procedural tasks are to be carrying out according to the information contained on received contingency events. To make decisions, plans of this capability take also into account current states and intentions of aircraft traffic. Information about the procedural tasks that have to be performed are included into contingency output events that will trigger specific plans to execute such tasks. These plans are grouped into the ATC Procedures Management capability that is described next.
Manage ATC Procedures. Plans of this capability carry out strategic and tactical actions aimed to maintain aircraft separation. These plans are grouped into the next for sub-capabilities:
Implementation of ATC procedures. This capability has a first plan that take into account air traffic conditions to generate events that trigger plans for: (i) ATC coordination in order to receipt o transfer air traffic, (ii) planning and assigning trajectories, (iii) establishing point for initiate air-ground negotiations and (iv) assuming or delegating aircraft separation responsibilities.
Strategic Separation. This capability is modelled through two basic plans. One of them drives processes of trajectories negotiation. The second one manages other supplemental plans that implement re-negotiation processes of trajectories previously assigned to a group of aircraft and pending of execution when a contingency arise.
Tactical Separation. Plans of this capability manage processes triggered by contingencies that require this type of action (e.g. separation loss contingency when air traffic flows converge). Obviously, in the context of TBO, tactical actions should be reduced to: (i) delegate or regain the separation control role depending as a function of the air traffic state and other contingences and (ii) enable separation control protocols in extreme short-range conflicts.
ATC Coordination. This capability includes plans to coordinate for air traffic transferring between adjacent ATC units. Events that trigger these plans come from the sub-capacity ATC procedure execution. The detailed design of this capability includes a specific plan to implement ATC coordination protocols with other adjacent units.
Descriptors and diagrams of the components in the described conceptual model contain all the necessary information to carry out implementation. However, not all the components obtained in the three phases of the methodology have to be implemented. The executable model consists of the entities that have been developed in the detailed design phase (i.e. agents, capabilities, plans, data, events and messages).
As it was explained in section 4, Prometheus methodology provides a full life-cycle support tool (PDT tool) to develop multi-agent systems. Current version of PDT provides support for: (i) designing most the design artefacts within the Prometheus methodology, (ii) cross-checking for consistency and completeness for the conceptual model, (iii) automatic generation of skeleton code in JACK agent-oriented programming language [48].
The conceptual model detailed above is currently at implementation phase. Although facilities of automatic code generation of PDT, we have opted for using the JADE Platform [39], cited in section 4, due to: (i) it is one of most extended multi-agent platforms and, (ii) it provides the FIPA standards [49] infrastructure for inter-agent communications and for managing software agents distributed across multiples hosts. As it was explained, architecture of JADE agent is built upon the behaviour concept rather than a plans-based architecture. Then agent plans can be generally implemented into JADE behaviours in a quite straightforward way.
On the other hand, continuous simulation requires, in nature, implementing the aircraft dynamic over a continuous-time model. It is essential to carry out real-time and human-in-the-loop simulations in order to analyse in detail and validate the design accordingly to the expected global behaviour. Also it is suitable for fast analytical simulations intended for preliminary designing and evaluation of cockpit systems and underlying mathematical models and algorithms (e.g. for 4D-trajectory guidance, conflicts detection and resolution, etc.). However, while the detailed models are not available, the proposed conceptual model enables discrete event simulation. In this case, events can be generated by random functions implemented within capabilities plans representing underlying models as black boxes. In this way, for an initial implementation phase, random functions to generate events are implemented into agent plans. In a later phase, the executable model can be refined when functions are replaced by specific underlying models as they are developed.
Figure 12 illustrates an adaptation of the ATC agent capacities-based architecture to a behaviours-based one using JADE behaviours described in section 4. Each one of the agent capabilities has been defined as behaviours that run in a parallel way. Previous behaviours could be progressively broken down into new behaviours, so that, at lower-level, behaviours correspond to plans of the conceptual model.
ATC agent architecture based on JADE Behaviours
As example of a JADE implementation we summarize the architecture of an Experimental Air Traffic Simulator (EATS) [32] that we have developed under a JADE support. This simulator includes agents described in the last section as well as other two agents with particular purposes into a simulation environment: the Configuration Agent and the Pseudopilot Agent.
The Configuration Agent is required to define the set of initial simulation parameters (e.g. aircraft type, available routes, etc. The Pseudopilot Agent has been designed with a twofold purpose. First, it is a desk control that allows to an unique pilot-user (named pseudopilot) to have control over several aircraft. Second, it represents a graphical display, providing significant information about the state and intentions of surrounding traffic for each selected aircraft. This interface has been implemented as a separated agent (and not like an aircraft agent component), to centralize in a unique interface the access to each aircraft. Besides, it plays an important role (especially in the near future scenarios) to design and evaluate specific on-board man-machine interfaces like the CDTI cockpit display [50]. The CDTI allows seeing the surrounding traffic and, what is more relevant, the intentions of the surrounding aircraft. To access to a particular aircraft, a mouse click over the icon symbol is required. Once the aircraft is selected, it is placed at the central position of the pseudopilot view window, and the movement and the position of other aircraft are represented in relation to it. At the same time, the control window of the selected aircraft will be opened.
Then air-traffic controllers and pilots can interact with agents by means of two types of consoles. In one of them an Air Traffic Controller can monitor the positions of different aircraft and send several data instructions to a specific aircraft. In the other one a user pseudopilot that receives orders from the ATC (via voice or via data messages), carries out the necessary actions to fly the aircraft according to these orders. Besides, pseudopilot agent can be configured to automatically execute ATC mentioned data instructions.
Figure 13 shows screenshot of this application. It represents a view of the ATC interface constituted by and screen for displaying the traffic and a window console for interchanging data and instructions with a particular aircraft. Besides, in the same screenshot two aircraft control windows (A320 and Cessna) are deployed.
Application screenshot for the described scenario
To carry out communications between agents, a Communication class with specific methods has been designed. In particular, the air-ground communication between the aircraft agents and the ATC agent is carried out with the following messages:
Messages sent by the aircraft to the ATC: message to inform about the state vector and planed route, message to inform about the possible modification of the altitudes of the flight plan to initiate a continuous descent approach to the airport.
Messages received in the aircraft from the ATC: instruction messages (changing altitude, heading, speed, a flight plan waypoint, etc.) and messages of conflict detection with other aircraft. Starting from this nucleus of air-ground communications, new types of messages can be implemented in future EATS extensions with the purpose of establishing more complex negotiations between aircraft and ATC.
Messages sent by the aircraft to others aircraft: message to inform about the state vector. This air-air communication provides information to each aircraft about its surrounding air traffic.
Besides the previous communications, there are other communications involving the Meteorology Information Provider agent (to obtain atmospheric information) and the Airspace Recourse Provider agent (to request the available routes). Moreover, the communication between the aircraft agent and the pseudo pilot agent represents the communication between a physicals agent (the aircraft) and a man-machine interface like the CDTI.
Apart from above infrastructure for agent communications, the current prototype version implements the aircraft aerodynamic and simple agent making-decision mechanisms. Thus, aircraft agents are able to fly according a three dimensional flight plans or ATC vector instructions (i.e. heading, altitude and speeds orders). The aircraft aerodynamic model is based on a simplified point mass model that is described in [51]. In addition some navigation coordination tasks have been implemented on it (e.g. modifying arrival flight plan to perform a continuous descent and communicate this modification to the ATC agent via data message). In the same way, the ATC agent can detect missed separation conflict and provide primary conflicts resolution
Then, this architecture is intended as later extensions in order to add new algorithms (i.e. conflict detection and resolution algorithms, arrival sequence algorithms, etc.), air-air and air-ground negotiations protocols, human-machine interfaces and decision-making support systems, etc.
A summary description of a proposed conceptual model that represents TBO scenarios as a multi-agent system has been presented in this chapter. The aim of this design was to illustrate how current agent-oriented methodologies have been successfully applied to achieve highly structured representations of these scenarios and, therefore, they are a powerful design tool previous to a full implementation of future operational concepts.
A practical and formal methodological approach has been used to analyse and design the mentioned scenarios in a structured and consistent manner. By means of an iterative top-down modelling process the detailed agents architecture were designed based on capabilities, plans, events, and data structures.
The ATC view point was also described in this chapter through its inner architecture design. This architecture is oriented to execute several processes in order to plan, execute or modify trajectories in a coordinated way.
This approach has been based on guidelines provides by the recent Prometheus methodology. It has showed to be a suitable methodology for building models of next-generation ATM systems in the light of the following features:
This approach achieves the goals of obtaining a highly structured model with several levels of abstractions. This structured nature allows a suitable integrity and consistence verification of the model on each of its three design phases: system specification, architecture design and detailed design.
Also, Prometheus methodology provides proper guidelines for obtaining a system specification based on a goals hierarchical structure. These goals were identified from an organized set of scenarios that illustrate several aspects of the operational behaviour of the system. Goal at lowest level are used for defining required functionalities of the system as well as its main data, actions and perceptions.
The overall system architecture combines information about roles of the air traffic entities with communication protocols that agents needs in order to improve their knowledge about the environment, agents states and intentions. Protocols are, also, a key aspect into the agent negotiation processes to achieve their respective goals.
It illustrates how agent processes can be implemented by a set of several plans into agent. Plans are organized into several capabilities. Besides, this modular agent architecture based on plans allows a latter inclusion of new plans for implementing new procedures and functionalities. Moreover it is particularly important for obtaining robust software models.
Finally, the model connects in a natural way those components that represent the dynamic perspective from those one than give a structural vision of the model. Protocols and processes, that model the dynamic behaviour, represent the core of the procedures. In the other hand capabilities are a high level representation of the systems required by ATC, aircraft and other air traffic entities to execute their task in a procedural manner.
After this first version of a simulation platform has been implemented and validated, new procedures, functionalities and underlying models will be included to be analysed as they are designed and included in the system following the described methodology. Furthermore, directions for future works include the extension of this conceptual model to gate-to-gate operations, as well as obtaining a full executable model for analytical simulation according to the described requirements.
This work was funded by Spanish Ministry of Economy and Competitiveness under grant TEC2011-28626 C01-C02, and by the Government of Madrid under grant S2009/TIC-1485 (CONTEXTS).
Solar energy is one of the primary sources in the field of green and pure energy that points to the power predicament and climate change task. Solar energy consumption is an ecological reconciliation, and then, the chemical change in solar is presence exhaustive, considered throughout global [1, 2]. In general, solar energy is renewed into a wide range of developments, such as degradation of organic pollutants as photocatalysis, splitting of water molecules for producing clean energy, and reduction of CO2 gas [3, 4]. Consuming a similar perception, metal-oxide photocatalysis has also been widely examined for possible exertions in ecological restitution as well as the photodegradation and elimination of organic toxins in the aquatic system [5, 6], decrease of bacterial inactivation [7, 8, 9], and heavy metal ions [10, 11, 12]. Throughout the earlier few years, excellent applications have been dedicated to evolving well-organized, less expensive, and substantial photocatalysts, particularly those that can become active under visible light such as NaLaTiO6, Ag3PO4/BaTiO3, Pt/SrTiO3, SrTiO3-TiN, noble-metal-SrTiO3 composites, GdCoO3, orthorhombic perovskites LnVO3 and Ln1−xTixVO3 (Ln = Ce, Pr, and Nd), Ca0.6Ho0.4MnO3, Ce-doped BaTiO3, fluorinated Bi2WO6, graphitic carbon nitride-Bi2WO6, BaZrO3−δ, CaCu3Ti4O12, [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], graphene-doped perovskite materials, and nonmetal-doped perovskites [25]. Furthermore, directed to years extended exhaustive investigation exertions on the pursuit of innovative photocatalytic systems, particularly that can produce the overall spectrum of visible-light. Out of a vast assemblage of photocatalysts, perovskite or layered-type perovskite systems and its analogs include a better candidate for capable semiconductor-based photocatalysts due to their framework easiness and versatility, excellent photostability, and systematic photocatalytic nature. In general, the ideal perovskite structure is cubic, and the formula is ABO3. Where A is different metal cations having charge +1 or +2 or +3 nature and B site occupies with tri or tetra and pentavalent nature, which covers the whole family of perovskite oxide materials by sensibly various metal ions at A and B locations [26], aside from a perfect cubic perovskite system, basic alteration perhaps persuaded by several cations exchange. Such framework alteration could undoubtedly vary the photophysical, optical, and photocatalytic activities of primary oxides.
Moreover, a sequence of layered-type perovskite materials contains many 2D blocks of the ABO3 framework, which are parted by fixed blocks. The scope of formulating multicomponent perovskite systems by whichever fractional change of cations in A and B or both positions or injecting perovskite oxides into a layered-type framework agrees scientists investigate and control the framework of crystals and the correlated electronic and photocatalytic activities of the perovskites. So far, hundreds of various types of perovskite or perovskite-based catalysts have been published, and more outstandingly, some ABO3-related materials became renowned with “referred” accomplishment for catalytic activities. Thus, these systems (perovskite materials) have exposed highly capable of upcoming applications on the source of applying more attempts to them. While several outstanding reviews mean that explained that perovskites performed as photocatalyst for degradation of organic pollutants [27, 28, 29, 30], only an insufficient of them content consideration to inorganic perovskite (mostly ABO3-related) photocatalysts [31, 32, 33]. A wide range of tagging and complete attention of perovskite materials, for example, layered-type perovskite acting as photocatalysts, is relatively deficient. The purpose of this book chapter is to precise the current progress of perovskite-based photocatalysts for ecological reparation, deliberate current results, and development on perovskite oxides as catalysts, and allow a view on the upcoming investigation of perovskite materials. After a short outline on the wide-ranging structure of perovskite oxides, it was stated that perovskites act as a photocatalyst that are incorporated, arranged and explored based on preparation methods [29, 34], photophysical properties based on bandgap energies, morphology-based framework and the photocatalytic activities depends on either UV or visible light energy of the semiconducting materials. Finally, this chapter is based on the current advancement and expansion of perovskite photocatalytic applications under solar energy consumption. The potential utilization, new tasks, and the research pathway will be accounted for the final part of the chapter [35].
The standard system of perovskite-based materials could be designated as ABO3, where the A and B are cations with 12-fold coordinated and 6-fold coordinated to concerning oxygen anions. Figure 1a describes the typically coordinated basic of the ABO3 system, which consists of a 3D system, BO6 octahedra as located at corner, and at the center, A cation are occupied. Within the ABO3 system, the A cation usually is group I and II or a lanthanide metal, whereas the B is commonly a transition metal ion. The tolerance factor (t) = 1 calculated by using an equation t = (rA + rO)/ √ 2 (rB + rO), where rO, rA, and rB are the radii of respective ions A and B and oxygen elements for a cubic crystal structure ABO3 perovskite system [36].
Both crystal and layered type perovskite oxides (blue small balls: A-site element; dark blue squares: BO6 octahedra with green and red balls are oxygen).
For constituting a stable perovskite, it is typically the range of t value present in between 0.75 and 1.0. The lower value of t builds a somewhat slanted perovskite framework with rhombohedral or orthorhombic symmetry. In the case of t, it is approximately 1; then, perovskite structure is an ideal cubic system at high temperatures. Even though the value of t, obtained by the size of metal ion, is a significant guide for the permanency of perovskite systems, the factor of octahedral (u) u = rB/rO and the role of the metal ions composition of A and B atoms and the coordination number of respective metals are considered [37]. Given the account of those manipulating factors and the electro-neutrality, the ABO3 perovskite can hold a broad variety of sets of A and B by equal or dissimilar oxidation states and ionic radii. Moreover, the replacement of A or B as well as both the cations could be partly by the doping of various elements, to range the ABO3 perovskite into a wide-ranging family of Am1A1−m1 B1nB1−n1 O3±δ [38]. The replacement of several cations into the either A or B positions could modify the structure of the original system and therefore improve the photocatalytic activities [23]. After various metal ions in perovskite oxide are doped, the optical and electronic band positions, which influence the high impact on the photocatalytic process, are modified [24].
Moreover, to the overall ABO3 system, further characteristic polymorphs of the perovskite system are Brownmillerite (BM) (A2B2O5) framework [39]. BM is a type of oxygen-deficient perovskite, in which the unit cell is a system of well-organized BO4 and BO6 units. The coordination number of cations occupied by A-site was decreased to eight because of the oxygen deficiency. Perovskite (ABO3) oxides have three dissimilar ionic groups, construction for varied and possibly useful imperfection chemistry. Moreover, the partial replacement of A and B ions is permitted even though conserving the perovskite system and shortages of cations at the A-site or of oxygen anions are common [40]. The Ion-exchange method is used for the replacement of existing metal ions with similar sized or dissimilar oxidation states; then, imperfections can be announced into the system. The imperfection concentrations of perovskites could be led by doping of different cations [24]. Oxygen ion interstitials or vacancies could be formed by the replacement of B-position cations with higher or lower valence, respectively, fabricating new compounds of AB(1−m)BmIO3−δ [41]. A typical oxygen-deficient perovskite system is Brownmillerite (A2B2O5), in which one part of six oxygen atoms is eliminated. Moreover, the replacement of exciting a site cation to new cation with higher oxidation state metal ions then the formed new materials with new framework with different stoichiometry is A1−mAmIBO3 [41]. In the case of the replacement of A-site ions with smaller oxidation state cations, consequences in oxygen-deficient materials with new framework such as A1−mAmIBO3−x are developed. Thermodynamically, the replacement of B-position vacancies in perovskite systems is not preferable due to the compact size and the high charge of B cations [42]. A-position vacancies are more detected due to the BO3 range in perovskite system forms a stable network [43]; the 12 coordinated sites can be partly absent due to bigger-size A cations. Lately, presenting suitable imperfections on top of the surface of perovskite oxides has been thoroughly examined as a means of varying the bands’ position and optical properties of the starting materials. For this reason, perovskite materials afford a tremendous objective for imperfection originating to vary the photocatalytic activity of perovskite material-based photocatalysts [44].
The typical formula for the furthermost recognized layered perovskite materials is designated as An+1BnO3n+1 or A2IAn−1BnO3n+1 (Ruddlesden-Popper (RP) phase), AI[An−1BnO3n+1] (Dion-Jacobson (DJ) phase) for {100} series, (AnBnO3n+2) for {110} series and (An+1BnO3n+3) for {111}, and (Bi2O2)(An−1BnO3n+1) (Aurivillius phase) series. In these systems, n represents the number of BO6 octahedra that duration a layer, which describes the width of the layer. Typical samples of these layered systems are revealed in Figure 1c–g. For RP phases, their frameworks consist of AIO as the spacing layer for the intergrowth ABO3 system. These materials hold fascinating properties such as ferroelectricity, superconductivity, magnetoresistance, and photocatalytic activity. Sr2SnO4 and Li2CaTa2O7 systems are materials of simple RP kind photocatalysts. A common formula for DJ phase is AI[An−1BnO3n+1] (n > 1), where AI splits the perovskite-type slabs and is characteristically a monovalent alkali cation. The typical DJ kind photocatalysts are RbLnTa2O7 (n = 2) and KCa2Nb3O10 (n = 3). Associates of the AnBnO3n+2 and An+1BnO3n+3 structural sequences with dissimilar layered alignments have also been recognized in some photocatalysts like Sr2Ta2O7 and Sr5Ta4O15 (n = 4). For Aurivillius phases, their frameworks are constructed by one after another fluctuating layers of [Bi2O2]2+ and virtual perovskite blocks. Bi2WO6 and BiMoO6 (n = 1), found as the primary ferroelectric nature for Aurivillius materials, lately have been extensively investigated as visible light photocatalysts.
A broad array of perovskite photocatalysts have been advanced for organic pollutant degradation in the presence of ultraviolet or visible-light-driven through the last two decades [45]. These typical examples and brief investigational consequences on perovskites are concise giving to their systems, then perovskite materials categorized into six groups. Precisely, ABO3-type perovskites, AAIBO3, AIABO3, ABBIO3 and AB(ON)3-type perovskites, and AAIBBIIO3-type perovskites are listed in Table 1.
Perovskite system | Synthesis process | Light source | Pollutants | References |
---|---|---|---|---|
NaTaO3 | HT | UV | CH3CHO | [163] |
La-doped NaTaO3 | SG | UV | MB | [164] |
La-doped NaTaO3 | HT | UV | MB | [165] |
Cr-doped NaTaO3 | HT | UV | MB | [166] |
Eu-doped NaTaO3 | SS | UV | MB | [167] |
Bi-doped NaTaO3 | SS | UV | MB | [168] |
N-doped NaTaO3 | SS | UV | MB | [169] |
C-doped NaTaO3 | HT | Visible | NOx | [36] |
N/F co-doped NaTaO3 | HT | UV | RhB | [170] |
SrTiO3 | HT | UV | RhB | [42, 43, 171] |
Fe-doped SrTiO3 | SG | Visible | RhB | [172] |
N-doped SrTiO3 | HT | Visible | MB, RhB, MO | [173] |
F-doped SrTiO3 | BM | Visible | NO | [174] |
Ni/La-doped SrTiO3 | SG | Visible | MG | [175] |
S/C co-doped SrTiO3 | SS | Visible | 2-Propanol | [176] |
N/La-doped SrTiO3 | SG | Visible | 2-Propanol | [177] |
Fe-doped SrTiO3 | ST | Visible light | TC | [178] |
SrTiO3/Fe2O3 | HT | Visible | TC | [179] |
BaTiO3 | SG | UV | Pesticide | [36] |
BaTiO3 | SG | UV | Aromatics | [58] |
BaTiO3 | HT | UV | MO | [58] |
KNbO3 | HT | Visible | RhB | [180] |
KNbO3 | HT | UV | RhB | [181] |
KNbO3 | HT | Visible | MB | [182] |
NaNbO3 | SS | UV | RhB | [183] |
NaNbO3 | Imp. | UV | 2-Propanol | [184] |
NaNbO3 | SS | UV | MB | [185] |
N-doped NaNbO3 | SS | UV | 2-Propanol | [186, 187, 188] |
Ru-doped NaNbO3 | HT | Visible | Phenol | [189] |
AgNbO3 | SS | UV | MB | [190] |
La-doped AgNbO3 | SS | Visible | 2-Propanol | [191] |
BiFeO3 | SG | UV-Vis | MO, RhB, 4-CP | [69– 77] |
Ba-doped BiFeO3 | ES | Visible | CR | [79] |
Ca-doped BiFeO3 | ES | Visible | CR | [80] |
Ba or Mn-doped BiFeO3 | ES | Visible | CR | [82] |
Ca or Mn-doped BiFeO3 | HT | UV- Visible | RhB | [82] |
Gd-doped BiFeO3 | SG | Visible | RhB | [83] |
LaFeO3 | Comb. | UV | Methyl phenol | [84] |
LaFeO3 | SG | Visible | RhB | [85] |
LaFeO3 | HT | Visible | RhB, MB, chlorophenol | [86, 90, 91, 192] |
Ca-doped LaFeO3 | SS | Visible | MB | [92] |
LnFeO3 (Pr,Y) | SG | Visible | RhB | [193] |
SrFeO3−x | US | Visible | Phenol | [194] |
SrFeO3 | SS | Visible | MB | [195] |
BaZrO3 | SG | UV | MB | [196] |
BaZrO3 | HT | UV | MO | [197] |
ATiO3 (A = Fe, Pb) and AFeO3 (A = Bi, La, Y) | SG | Visible | MB | [198] |
Zn0.9Mg0.1TiO3 | SG | Visible | MB | [199] |
SrTiO3 nanocube-coated CdS microspheres | HT | Visible | Antibiotic pollutants | [200] |
Ag/AgCl/CaTiO3 | HT | Visible | RhB | [201] |
TiO2-coupled NiTiO3 | SS | Visible | MB | [202] |
ZnTiO3 | HT | UV | MO and PCP | [203] |
Mg-doped BaZrO3 | SS | UV | MB | [204] |
SrSnO3 | MW | UV | MO | [205] |
LaCoO3 | MW | Visible | MO | [206] |
LaCoO3 | Ads. | UV | MB, MO | [207] |
LaCoO3 | ES | UV | RhB | [208] |
LaNiO3 | SG | Visible | MO | [209] |
Bi0.5Na0.5TiO3 | HT | UV | MO | [93] |
La0.7Sr0.3MnO3 | SG | Solar light | MO | [97] |
La0.5Ca0.5NiO3 | SG | UV | RB5 | [98] |
La0.5Ca0.5CoO3 | SG | UV | CR | [99] |
Sr1−xBaxSnO3 | SS | UV | Azo-dye | [100] |
BaCo1/2Nb1/2O3 | SG | Visible | MB | [210] |
Ba(In1/3Pb1/3M1/3)O3 (M = Nb and Ta) | SS | Visible | MB, 4-CP | [211] |
A(In1/3Nb1/3B1/3)O3 (A = Sr, Ba; B = Sn, Pb) | SS | Visible | MB, 4-CP | [212] |
SrTi1−xFexO3−δ | SS | Visible | MB | [102] |
SrTi0.1Fe0.9O3−δ | SG | Solar light | MO | [103] |
SrFe0.5Co0.5O3−δ | SG | Solar light | CR | [213] |
LaFe0.5Ti0.5O3 | SG | UV | Phenol | [90] |
Bi(Mg3/8Fe2/8Ti3/8)O3 | MS | Visible | MO | [110] |
LaTi(ON)3 | SG | Visible | Acetone | [214] |
(Ag0.75Sr0.25)(Nb0.75Ti0.25)O3 | SS | Visible | CH3CHO | [215] |
La0.8Ba0.2Fe0.9Mn0.1O3−x | SG | Solar light | MO | [115] |
Cu-(Sr1−yNay)(Ti1−xMox)O3 | HT | Visible | Propanol | [118] |
BiFeO3–(Na0.5Bi0.5)TiO3 | SG | Visible | RhB | [120] |
SrBi2Nb2O9 | SG SS | UV | Aniline, RhB | [145, 146] |
ABi2Nb2O9 (A = Sr, Ba) | SG | UV | MO | [147] |
Bi5Ti3FeO15 | HT SS | Visible | RhB, CH3CHO IPA | [149, 150] |
Bi5−xLaxTi3FeO15 | SS | Solar light | RhB | [151] |
Bi3SrTi2TaO12 Bi2LaSrTi2TaO12 | SS | UV | RhB | [216] |
Ba5Ta4O15 | HT | UV | RhB | [154] |
N-doped Ln2Ti2O7 (Ln = La, Pr, Nd) | HT | Visible | MO | [217] |
CdS/Ag/Bi2MoO6 | SG | Visible | RhB | [218] |
Perovskite materials used as photocatalysts (ABO3, AAIBO3, AAIBO3, ABBIO3, AB(ON)3, and AAIBBIIO3) for degradation of pollutants.
SS: solid state; HT: hydrothermal; SG: sol-gel; BM: ball-milling; ES: electronspun; MW: microwave; Comb.: combustion; US: ultrasonic; MS: molten salt; Imp.: impregnation; Ads.: adsorption; ST: solvothermal; RhB: rhodamine B; MO: methyl orange; MB: methylene blue; 4-cp: 4-chlorophenol; MG: malachite green; CR: congo red; NO: nitrogen monoxide; PA: isopropyl alcohol; TC: tetracycline; and PCP: pentachlorophenol.
NaTaO3 has been a standard perovskite material for a well-organized UV-light photocatalyst for degradation of organic pollutants and production of H2 and O2 through water splitting [46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]. It can be prepared by various methods such as solid-state [46, 47, 48, 53, 56], hydrothermal [49, 52, 54, 55], molten salt [57] and sol-gel [50, 51] and with wide bandgap of 4.0 eV. In order to enhance the surface area of NaTaO3 bulk material, many investigators tried to use further synthetic ways to make nanosized particles as an additional study on the NaTaO3 photocatalyst for degradation of organic pollutants. Kondo et al. prepared a colloidal range of NaTaO3 nanoparticles consuming three-dimensional mesoporous carbon as a pattern, which was pretend by the colloidal arrangement of silica nanospheres. After calcining the mesoporous carbon matrix, a colloidal arrangement of NaTaO3 nanoparticles with a range of 20 nm and a surface area of 34 m2 g−1 was attained. C-doped NaTaO3 material was tested for degradation of NOx under UV light [36]. Several titanates such as BaTiO3 [58, 59, 60], Rh or Fe-doped BaTiO3 [61, 62], CaTiO3 [63, 64] and Cu [65], Rh [66], Ag and La-doped CaTiO3 [67], and PbTiO3 [68, 69] were also described as UV or visible light photocatalysts. Magnetic BiFeO3, recognized as the one of the multi-ferric perovskite materials in magnetoelectric properties, was also examined as a visible light photocatalyst for photodegradation of organic pollutants because of small bandgap energy (2.2 eV) [70, 71, 72, 73, 74, 75, 76, 77, 78, 79]. In a previous account, BiFeO3 with a bandgap of around 2.18 eV produced by a citric acid-supported sol-gel technique has revealed its visible-light-driven photocatalytic study by the disintegration of methyl orange dye [70]. The subsequent investigations on BiFeO3 are primarily concentrated on the synthesis of new framework BiFeO3 with various morphologies. For instance, Lin and Nan et al. prepared BiFeO3 unvarying microspheres and microcubes by a using hydrothermal technique as revealed in Figure 2 [73].
SEM patterns of BiFeO3: (a) microspheres and (b) microcubes. The intensified pictures are revealed in the upper part inserts. Recopied with consent from Ref. [147]. Copyright © 2010, American Chemical Society.
The bandgap energies of BiFeO3 compounds were found to be about 1.82 eV for BiFeO3 microspheres and 2.12–2.27 eV for microcubes. This indicated that the absorption edge was moved toward the longer wavelength that is influenced by the crystal-field strength, particle size, and morphology. The microcube material showed the maximum photocatalytic degradation performance of congo red dye under visible-light irradiation due to the quite low bandgap energy. Further, a simplistic aerosol-spraying method was established for the synthesis of mesoporous BiFeO3 hollow spheres with improved activity for the photodegradation of RhB dye and 4-chlorophenol, because of improved light absorbance ensuing from various light reflections in a hollow chamber and a very high surface area [71]. Moreover, a unusually improved water oxidation property on Au nanoparticle-filled BiFeO3 nanowires under visible-light-driven was described [77]. The Au-BiFeO3 hybrid system was encouraged by the electrostatic contact of negatively charged Au nanoparticles and positively charged BiFeO3 nanowires at pH 6.0 giving to their various isoelectric points. An improved absorbance between 500 and 600 nm was found for Au/BiFeO3 systems because of the characteristic Au surface plasmon band existing visible light region then which greater influenced in the photodegradation of organic pollutants. Also, the study of photoluminescence supported improvement of the photocatalytic property due to the effective charge transfer from BiFeO3 to Au. Even though Ba, Ca, Mn, and Gd-doped BiFeO3 nanomaterials have exhibited noticeable photocatalytic property for the degradation of dyes [80, 81, 82, 83, 84], several nano-based LaFeO3 with various morphologies such as nanoparticles, nanorods, nanotubes, nanosheets, and nanospheres have also been synthesized for visible light photocatalysts for degradation of organic dyes [85, 86, 87, 88, 89, 90, 91, 92, 93]. Sodium bismuth titanate (Bi0.5Na0.5TiO3) has been extensively used for ferroelectric and piezoelectric devices. It was also investigated as a UV-light photocatalyst with a bandgap energy of 3.0 eV [94, 95, 96, 97]. Hierarchical micro/nanostructured Bi0.5Na0.5TiO3 was produced by in situ self-assembly of Bi0.5Na0.5TiO3 nanocrystals under precise hydrothermal conditions, through the evolution mechanism was examined in aspect means that during which the growth mechanism was studied [95]. It was anticipated that the hierarchical nanostructure was assembled through a method of nucleation and growth and accumulation of nanoparticles and following in situ dissolution-recrystallization of the microsphere type nanoparticles with extended heating period and enhanced temperature or basic settings. The 3D hierarchical Bi0.5Na0.5TiO3 showed very high photocatalytic activity for the decomposition of methyl orange dye because of the adsorption of dye molecules and bigger surface area. The properties of Bi0.5Na0.5TiO3 were also assessed by photocatalytic degradation of nitric oxide in the gas phase [95]. La0.7Sr0.3MnO3, acting as a photocatalyst, was examined for solar light-based photocatalytic decomposition of methyl orange [96, 97, 98]. In addition, La0.5Ca0.5NiO3 [99], La0.5Ca0.5CoO3−δ [100], and Sr1−xBaxSnO3 (x = 0–1) [101] nanoparticles were synthesized for revealing improved photocatalytic degradation of dyes. A-site strontium-based perovskites such as SrTi1−xFexO3−δ, SrTi0.1Fe0.9O3−δ, SrNb0.5Fe0.5O3, and SrCo0.5Fe0.5O3−δ compounds were prepared through solid-state reaction and sol-gel approaches, and were examined for the degradation of organic pollutants under visible light irradiation [102, 103, 104, 105]. Also, some other researchers modified A-site with lanthanum-based perovskites such as LaNi1−xCuxO3 and LaFe0.5Ti0.5O3 were confirmed as effective visible light photocatalysts for the photodegradation of p-chlorophenol [91, 106, 107]. The other ABBIO3 kind photocatalysts with Ca(TiZr)O3 [108], Ba(ZrSn)O3 [109], Na(BiTa)O3 [110], Na(TiCu)O3 [111], Bi(MgFeTi)O3 [112], and Ag(TaNb)O3 [113] have also been studied. Related to AAIBO3-type perovskites, the ABBIO3 kind system means that BI-site substitution by a different cation is another option for tuning the physicochemical or photocatalytic properties of perovskites materials as photocatalyst, due to typically the B-position cations in ABO3 mostly regulate the position of the conduction band, moreover to construct the structure of perovskite system with oxygen atoms. The band positions of photocatalyst can be magnificently modified by sensibly coalescing dual or ternary metal cations at the B-position, or changing the ratio of several cations, which has been fine verified by the various materials as mentioned above. More studies on ABBIO3 kind of photocatalysts are projected to show their new exhilarating photocatalytic efficiency.
The mesoporous nature of LaTiO2N of photocatalyst attended due to thermal ammonolysis process of La2Ti2O7 precursor from polymer complex obtained from the solid-state reaction. The oxynitride analysis revealed that the pore size and shape, lattice defects and local defects, and oxidation states’ local analysis related between morphology and photocatalytic activity were reported by Pokrant et al. [114]. Due to the high capability of accommodating an extensive array of cations and valences at both A- and B-sites, ABO3-kind perovskite materials are capable materials for fabricating solid-solution photocatalysts. On the other hand, equally the A and B cations can be changed by corresponding cations subsequent in a perovskite with the formula of (ABO3)x(AIBIO3)1−x. Additional solid solution examples with CaZrO3–CaTaO2N [115], SrTiO3–LaTiO2N [116], La0.8Ba0.2Fe0.9Mn0.1O3−x [117], Na1−xLaxFe1−xTaxO3 [118], Na0.5La0.5TiO3–LaCrO3 [119], Cu-(Sr1−yNay)-(Ti1−xMox)O3 [120], Na1−xLaxTa1−xCrxO3 [121], BiFeO3–(Na0.5Bi0.5)TiO3 [122], and Sr1−xBixTi1−xCrxO3 [123] have been used as photocatalysts for splitting of water molecules under visible light.
In the general formula of the RP phase, An−1A2IBnO3n+1, A and AI are alkali, alkaline earth, or rare earth metals, respectively, while B states to transition metals. A and AI cations are placed in the perovskite layer and boundary with 12-fold cuboctahedral and 9-fold coordination to the anions, respectively, whereas B cations are sited inside the perovskite system with anionic squares, octahedra, and pyramids. The tantalum-based RP phase materials have been examined as photocatalysts for degradation of organic pollutants under UV light irradiation conditions; such materials are K2Sr1.5Ta3O10 [124], Li2CaTa2O7 [125], H1.81Sr0.81Bi0.19Ta2O7 [126], and N-alkyl chain inserted H2CaTa2O7 [127]. A series of various metals and N-doped perovskite materials were synthesized, such as Sn, Cr, Zn, V, Fe, Ni, W, and N-doped K2La2Ti3O10, for photocatalysis studies under UV and visible light irradiation [128, 129, 130, 131, 132, 133]. Still, only Sn-doping efficiently decreased the bandgap energy of K2La2Ti3O10 from 3.6 eV to 2.7 eV. The bandgap energy of N-doped K2La2Ti3O10 was measured to be around 3.4 eV. Additional RP phase kind titanates like Sr2SnO4 [134], Sr3Ti2O7 [135], Cr-doped Sr2TiO4 [136], Sr4Ti3O10 [137], Na2Ca2Nb4O13 [138], and Rh- and Ln-doped Ca3Ti2O7 [139] have also been examined. Bi2WO6 (2.8 eV) shows very high oxygen evolution efficacy than Bi2MoO6 (3.0 eV) from aqueous AgNO3 solution under visible-light-driven. Because of the appropriate bandgap energy, comparatively elevated photocatalytic performance, and good constancy, Bi2MO6 materials have been thoroughly examined as the Aurivillius phase kind that acts as photocatalysts under visible light. In this connection, hundreds of publications associated to the Bi2MoO6 and Bi2WO6 act as photocatalysts so far reported. Most of the investigations in the reports are concentrated on the synthesis of various nanostructured Bi2MoO6 and Bi2WO6 as well as nanofibers, nanosheets, ordered arrays, hollow spheres, hierarchical architectures, inverse opals, and nanoplates, etc., by various synthesis techniques like solvothermal, hydrothermal, electrospinning, molten salt, thermal evaporation deposition, and microwave. All these methods of hydrothermal process have been frequently working for the controlled sizes, shapes, and morphologies of the particles. The photocatalytic properties of these perovskite materials are mostly examined by the photodegradation of organic pollutants. Moreover, the investigations on the simple Bi2MoO6 and Bi2WO6, doped with various metals and nonmetals such as Zn, Er, Mo, Zr, Gd, W, F, and N, into Bi2MoO6 and Bi2WO6 was studied for increasing the photocatalytic performance under visible light. Therefore, these Bi2MO6-based photocatalysts is not specified here, due to further full deliberations that can be shown in many reviews [140, 141, 142].
ABi2Nb2O9 where A is Ca, Sr, Ba and Pb is other type of the AL-like layered perovskite material [143, 144, 145, 146, 147, 148, 149, 150]. The bandgap energy of PbBi2Nb2O9 is 2.88 eV and originally described as an undoped with single-phase layered-type perovskite material used as photocatalyst employed under visible light irradiation [144]. Bi5FeTi3O15 is also Aurivillius (AL) type multi-layered nanostructured perovskite material with a low bandgap energy (2.1 eV) and also shows photocatalytic activity under visible light [151, 152]. Mostly, these materials were synthesized using the hydrothermal method that has been frequently working for the controlled shapes such as flower-like hierarchical morphology, nanoplate-based, and the complete advance process from nanonet-based to nanoplate-based micro-flowers was shown. The photocatalytic activity of Bi5FeTi3O15 was studied by the degradation of rhodamine B and acetaldehyde under visible light [151]. The La substituted Bi5−xLaxTi3FeO15 (x = 1, 2) Al-type layered materials were synthesized through hydrothermal method and these materials were used for photodegradation of rhodamine B under solar-light irradiation [153]. Among all AL-type perovskite materials, only PbBi2Nb2O9, Bi2MO6 (M = W or Mo), and Bi5Ti3FeO15 are very high photocatalytic active under visible-light-driven due to low bandgap energy and photostability. Another type of layered perovskite material is Dion-Jacobson phase (DJ), a simple example is CsBa2M3O10 (M = Ta, Nb) and oxynitride crystals used for degradation of caffeine from wastewater under UVA- and visible-light-driven [154]. Similarly, another DJ phase material such means Dion–Jacobsen (DJ) as CsM2Nb3O10 (M = Ba and Sr) and also doped with nitrogen used for photocatalysts for degradation of methylene blue [155]. Zhu et al. prepared tantalum-based {111}-layered type of perovskite material such as Ba5Ta4O15 from hydrothermal method, which has been frequently employed for the controlled shape like hexagonal structure with nanosheets and used as photocatalyst for photodegradation of rhodamine B and gaseous formaldehyde [156]. Pola et al. synthesized a layered-type perovskite material constructed on AIAIITi2O6 (AI = Na or Ag or Cu and AII = La) structure for the photodegradation of several organic pollutants and industrial wastewater under visible-light-driven [157, 158, 159, 160, 161, 162].
Authors would like to thank DST-FIST schemes and CSIR, New Delhi. One of us (Ramesh Gade) thanks Council of Scientific & Industrial Research (CSIR), New Delhi, for the award of Junior Research Fellowship.
I am thankful to Department of Chemistry, University College of Science, Osmania University, for their continuous attention in this study and useful discussions, and to Prof. B. Manohar for her support in working on the chapter.
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