Fuzzy sets for input and output variables.
\r\n\tOf high significance is also to define the role of novel biomarkers in the early diagnostic, prognosis and therapy of gastric cancer. Finally, there will be a presentation of the novel and future modalities of treatment, starting with endoscopic treatment (including the role of EMR, ESD), open surgery with DI or DII resection/ laparoscopic surgery, chemo- and radio-therapy, palliative methods, and future perspectives using molecular targeted therapies, immunotherapies, and other revolutionary treatments.
\r\n\tAdding novel information of high scientific content about gastric cancer management, this book aims to make a step forward in understanding the complexity of this field of research with practical implications, and to open new gates in future development of efficient tools for diagnostic and personalized treatment of gastric cancer patients.
In the recent years, wireless sensor networks (WSNs) have attained significant roles in various applications and have attracted the attention of researchers due to their complex, multifaceted requirements which often divulge inherent tradeoffs. A wireless sensor network is made up of sensor nodes connected through an ad hoc and self-configuring connectivity.
\nA wireless sensor network consists of a set of spatially distributed autonomous sensor nodes to monitor environmental parameters such as temperature, pressure, etc., and to cooperatively pass their data through the network to a primary location called sink. The sink of a WSN collects data from the sensor nodes and reports the same to users through the Internet or through any private virtual network (PVN). By nature, WSNs inherit features and requirements of an ad hoc network. WSN belongs to the class of low range wireless personal area network (LPWPAN).
\nA sensor node consists of a radio transceiver (which performs the role of both transmitter and receiver), a microcontroller, and an electronic circuit for interfacing with the associated sensors and an energy source (usually a battery or an embedded form of energy harvesting). The cost and size of sensor nodes show a significant degree of variation depending upon the nature of the applications. In many applications, sensor nodes demand self-organization due to the randomness present in uncontrolled, non-deterministic topologies.
\nDepending upon the nature of applications, various categories of sensor nodes are provided for monitoring parameters such as temperature, moisture, sound, motion of objects, etc. In essence, sensor networks compensate for human efforts in inaccessible terrains and present more comfortable, smart snapshots of the environment. In the recent future, sensor networks would conquer an integral part of human life and make existing personal computers, mobile communication devices and other computing devices less popular.
\nA sensor network may be composed of homogenous or heterogeneous sensor nodes. They may monitor either space or objects or interactions of these two. Today, sensor networks are employed in diversified fields such as battle field surveillance, medical diagnostics, precision agriculture, weather monitoring and home appliances control. Every sensor application demands its own set of requirements and characteristics. Some sensor applications employ reactors in the place of ordinary sensors to react to the events in an appropriate manner.
\nDesign of WSNs exhibits challenges due to the limited resources in terms of storage, processing and communication of messages. In most of the sensor applications, these resources become non-renewable also. Theoretical estimation could not be accurate enough in many scenarios to predict and prevent failure of sensor networks. The design complexity of WSNs increases with emerging applications and their requirements. Conventional algorithms designed for ad hoc networks are not good enough to cater to the needs of these sensor applications and this mandates new policies and protocols to be evolved.
\nWireless sensor networks can be classified into pre-deterministic and unattended networks based on the type of applications. Former category of networks gains the advantages of quality of service (QoS), fault tolerance, robustness and scalability. In many pragmatic scenarios, human supervision for sensor networks is limited or prohibited since the nodes are dispersed in critical environments such as deeper part of jungles and underwater environments. These networks are called as unattended networks.
\nAlso, an inherent trade-off is observed amidst the parameters used to determine the performance of a WSN. The applications seldom reconcile with identical set of parameters. Juxtaposing the requirements, they resist generalized solutions owing to their nature of self-contradiction and application-specific precincts. As the autonomy of nodes increase, scalability of the solutions is challenged. Research efforts made to improve throughput often result in increased overhead. There is a combined need for fast convergence time and minimum energy consumption of sensor nodes. When the solutions are inclined toward one or a set of parameters, they habitually compromise the rest of the performance factors. This intricacy leads to many interesting queries and solutions in describing the efficiency of a WSN. Slicing over the temporal and spatial domains, the process becomes more complex, multifaceted and highly specialized.
\nThe lifetime of a sensor network is stanchly dependent on the energy consumption, especially when there is no provision for human access to the involved sensor nodes. Hence, many methods have been proposed to minimize energy consumption in wireless sensor networks. The design of wireless sensor networks exhibits many challenges from this perspective.
\nThe performance of a wireless sensor network is not only multifaceted but also inherently imbalanced under one or limited angles of perception. A holistic and fair approach requires an unambiguous and complete understanding of sensor applications.
\nSensor nodes in an environment collect data and transmit it to a sink either directly or collaboratively through other nodes. Many sensor applications cluster the sensor nodes to achieve scalability, robustness and reduced network traffic.
\nA sample scenario of clustering is shown in Figure 1. Here, clusters are provided with cluster heads and these cluster heads transmit the aggregated data to the base station or the sink.
\nA clustered wireless sensor network.
The primary advantage of clustering is the scalability of performance across the expanding sensor networks. In addition to this, clustering approach provides numerous secondary advantages. It ensures reliability and avoids one-point failure due to its localized solutions. A clustering solution can suggest a sleep/wakeup schedule for a WSN to effectively reduce power consumption. In many sensor applications, all the sensor nodes are not required to be in wakeup state and consume energy. Based on the temporal and spatial dependencies, some sensor nodes can be put in sleep mode in which no energy is consumed. An effective schedule can be devised and communicated to these sensor nodes through the sink or administrator. Also, clustering ensures scalability of the application performance due to its semi-distributed nature.
\nAs indicated in the work done by Abbasi and Younis [1], clustering possesses certain challenges amidst its advantages. There are selected sensor nodes identified as “cluster heads,” which monitor and regulate the data flow across clusters for which considerable energy is consumed. Hence, the process of reclustering and reelection of cluster heads is required which results in the reduced lifetime of sensor networks.
\nOne of the conventional clustering protocols named low energy adaptive clustering hierarchy (LEACH) [2] addresses the overloading of clusters and it rotates the role of cluster heads among the sensor nodes present in a cluster. The significant drawback of this approach is that no weightage is given for the residual energy of the sensor nodes. The limitations of LEACH motivated researchers to revisit and improve the LEACH protocol to adopt QoS requirements of WSNs. In general, LEACH and its variants suffer from scalability and load balancing despite their simplicity.
\nAn energy aware clustering protocol using fuzzy logic (ECPF) [3] which is a hierarchical clustering protocol employs a fuzzy based system with the input variables namely, the node degree and the node centrality to form on-demand clusters. This work inspired many researchers to re-estimate the role of cluster heads from the intra-cluster perspective in a hierarchical sensor environment.
\nA clustering approach namely, energy aware clustering scheme with transmission power control for sensor networks (EACLE) [4] presents a distributed approach for path selection to reach the sink. This scheme sets different levels of transmission power for intra-cluster and inter-cluster communication to improve energy savings.
\nAn energy harvesting protocol namely, energy harvesting and information transmission protocol (EHITP) [5] estimates the energy to be harvested based on the outage probability.
\nThe aforementioned contemporary clustering approaches for wireless sensor networks indicate the need for the new clustering solutions from multiple perspectives.
\nThe advent of new technologies and emerging trends in application development challenge the research findings of performance in WSNs, especially from the energy perspective. A family of solutions is needed to work with various types of unattended sensor networks where many parameters are unpredictable due to the random deployment of sensor nodes. Observing closely, focus is required more on the communication overheads of sensor nodes. Also, any proposed clustering approach should be tested for its scalability in a WSN environment.
\nThe remaining part of this chapter presents the four proposed clustering approaches for wireless sensor networks. These approaches have improved the lifetime of sensor networks by taking advantages of techniques such as cognitive cluster head selection, support for energy harvesting, hierarchical clustering and effective sleep scheduling of sensor nodes. These have been presented in the following sections, titled, energy aware fuzzy clustering algorithm (EAFCA) [6], efficient energy harvesting assisted clustering (EEHC) algorithm [7], energy-efficient recursive clustering (EERC) algorithm [8] and adaptive distributed clustering algorithm (ADCA) [9], respectively.
\nEnergy aware fuzzy clustering algorithm (EAFCA) is a proposal based on cognitive technique for non-probabilistic clustering process. In this, the sensor nodes are assumed to be deployed in an unmanned wireless sensor application and clustered from the energy perspective.
\nThe following assumptions have been made on the experimental environment.
The sensor deployment is done in a random manner.
It is an unmanned sensor environment.
All the sensor nodes are kept static.
The distance between two sensor nodes is measured through the received signal strength.
After the sensor nodes are deployed, the distance between any two sensor nodes have to be computed. For this, our proposed approach employs a mechanism in which a beacon signal is transmitted from the sink to the rest of the sensor nodes. Based on the received signal strength, a sensor node can calculate its distance from the sink. Then a group of tentative cluster heads (TCHs) are elected from the sensor network for a specific fraction of the entire network as follows. A threshold “T” is calculated and forwarded to all the sensor nodes. Every sensor node generates a random number and compares the same against the received threshold value. Suppose the generated value is more than the threshold, then the sensor node declares itself as a cluster head. Otherwise, it becomes an ordinary sensor node.
\nThe proposed method results in 2-hop cluster formation and a permanent cluster head (CH) is elected based on fuzzy logic which emphasizes the following three factors:
\n(1) Remaining residual energy:
\nThis parameter is expected to be higher for an eligible CH in a competition phase as it is heavily engaged to intra-cluster and inter-cluster data traffic.
\n(2) Node degree at its 2-hop coverage:
\nThis parameter signifies the total number of neighbors which are located in the 2-hop distance from the tentative CH and is calculated as in Eq. (1). It is desirable for a tentative cluster head to have a higher value for this parameter to become a permanent cluster head.
\nwhere S2-hop-nbr(i) gives the total number of neighbors for the tentative cluster head in its 2-hop coverage. A typical 2-hop clustering environment in wireless sensor networks is represented in Figure 2 [6].
\nA 2-hop clustered wireless sensor network.
(3) Centrality of the CH:
\nFor an effective CH, this parameter should yield low values to reduce energy consumption during the data aggregation and flooding processes. Centrality of a node is calculated using Eq. (2).
\nwhere, the parameter “d(i,j)” represents the distance between nodes “i” and “j” in which node j is a member of the set 2-hop-nbr. The variable “A” represents area of the network.
\nUsing tentative cluster heads (TCHs) that are identified and by employing fuzzy logic, primary cluster heads (PCHs) are elected considering the aforementioned three parameters. The fuzzy output variable “chance” is computed for every tentative cluster head as shown in Table 1 to compute its probability to become to a permanent cluster head.
\nEnergy | \n2-hop ND | \nNode centrality | \nChance | \nEnergy | \n
---|---|---|---|---|
Low | \nLow | \nFar | \nVery weak | \nLow | \n
Low | \nLow | \nMedium | \nWeak | \nLow | \n
Low | \nLow | \nClose | \nLittle weak | \nLow | \n
Low | \nMedium | \nFar | \nWeak | \nLow | \n
Low | \nMedium | \nMedium | \nLittle weak | \nLow | \n
Low | \nMedium | \nClose | \nLittle weak | \nLow | \n
Low | \nHigh | \nFar | \nLittle weak | \nLow | \n
Low | \nHigh | \nMedium | \nLittle weak | \nLow | \n
Low | \nHigh | \nClose | \nMedium | \nLow | \n
Medium | \nLow | \nFar | \nLittle weak | \nMedium | \n
Medium | \nLow | \nMedium | \nLittle medium | \nMedium | \n
Medium | \nLow | \nClose | \nMedium | \nMedium | \n
Medium | \nMedium | \nFar | \nLittle medium | \nMedium | \n
Medium | \nMedium | \nMedium | \nMedium | \nMedium | \n
Medium | \nMedium | \nClose | \nHigh medium | \nMedium | \n
Medium | \nHigh | \nFar | \nMedium | \nMedium | \n
Medium | \nHigh | \nMedium | \nHigh medium | \nMedium | \n
Medium | \nHigh | \nClose | \nLittle strong | \nMedium | \n
High | \nLow | \nFar | \nMedium | \nHigh | \n
High | \nLow | \nMedium | \nHigh medium | \nHigh | \n
High | \nLow | \nClose | \nLittle strong | \nHigh | \n
High | \nMedium | \nFar | \nHigh medium | \nHigh | \n
High | \nMedium | \nMedium | \nLittle strong | \nHigh | \n
High | \nMedium | \nClose | \nStrong | \nHigh | \n
High | \nHigh | \nFar | \nLittle strong | \nHigh | \n
High | \nHigh | \nMedium | \nStrong | \nHigh | \n
High | \nHigh | \nClose | \nVery strong | \nHigh | \n
Fuzzy sets for input and output variables.
Using Mamdani method, the fuzzy if-then rules are processed. Every tentative cluster head obtains its chance values and broadcasts the same to all its 2-hop neighbors. It happens in subsequent stages of hop coverage.
\nThus a sensor node can either become a cluster head or remains as an ordinary node. An ordinary node has to identify the suitable cluster to join. Suppose it has received advertisements from two or more cluster heads to join, then it will choose the nearby cluster head and joins its group. This is done from the energy perspective. On certain occasions, it may receive advertisements from two cluster heads which are located at equal distances. In such case, the sensor node will choose the cluster head from whom the advertisement has been received earlier and joins. Thus overlapping of clustering is avoided.
\nFollowing the process of cluster formation, the data is generated from the sensor nodes. The cluster heads collect the data from the cluster nodes and aggregate the data. Here, a cluster is formed on 2-hop coverage and hence the aggregation can be done on a larger scale compared to the conventional 1-hop clustering approaches.
\nThe cluster heads have to report the aggregated data to the sink. Unlike LEACH and many conventional algorithms, the proposed algorithm inherits the presence of multi-hop relay between the cluster head and the sink. This multi-hop relay favors the selection of any one path based on certain probability among multiple choices and the selected path is not necessarily to be an optimal path as followed in many approaches. The repetitive employment of a few popular paths which have been identified as efficient paths causes energy depletion of nodes on these paths and pushes them die soon. Our idea of selecting less used or unused paths can effectively contribute to the distribution of energy consumption and prolong the lifetime of sensor nodes.
\nThe performance of the algorithm is evaluated under three different scenarios. In scenario 1, the sink is positioned at the center and 100 sensor nodes are deployed. In scenario 2, the sink is positioned at the center while the number of sensor nodes deployed was doubled as 200 to test the scalability of the network. In scenario 3, the sink is located outside the predefined WSN boundaries and the network size is maintained as in scenario 2. The performance of EAFCA algorithm is compared against the benchmarking protocols namely, low energy adaptive clustering hierarchy (LEACH), energy aware clustering protocol using fuzzy logic (ECPF) and energy aware clustering scheme with transmission power control for sensor networks (EACLE).
\nThe metrics employed for computing the lifetime of sensor networks are, first node dies (FND) and half of the nodes alive (HNA) metrics. These two metrics are widely adopted based on the viewpoint whether the energy depletion of the very first node in the network or half of the nodes indicate the death of the network. From the simulation results, it has been observed that the proposed algorithm shows 88% energy improvement compared to LEACH, 46% of improvement with respect to EACLE and 30% of improvement with respect to ECPF.
\nIt is to be observed that LEACH shows the poorest performance among the selected clustering approaches since it does not re-elect cluster heads from the energy perspective and it continues to be a probabilistic model. EACLE shows some improvement in energy consumption as studied from the simulation results. The gain in energy efficiency is achieved in EACLE since it employs multiple paths for inter-cluster traffic and postpones the death of sensor nodes. ECPF claims more energy improvement since it adopts a fuzzy based cluster head election. The results observed across both FND and HNA metric confirms this claim.
\nThe proposed EAFCA reduces energy efficiency by considering necessary and sufficient parameters for a cluster head election and assumes a feasible configuration in which 2-hop coverage is given for every cluster head and multi-hop relay is done for inter-cluster communication. The results demonstrate that EAFCA keeps WSN functioning for longer time than the other approaches.
\nThis work stands as a representative for cognitive and effective cluster head election process. Such strategies expose the sensor networks and its applications to the emerging era of explorations and can be eventually commercialized.
\nLifetime of wireless sensor applications depends upon the lifetime of the sensor nodes which are constrained by their energy resources. This can be managed by the use of energy harvesting, utilizing ambient sources to prolong the life of the batteries in wireless sensor nodes. The efficiency of this approach depends upon how much energy is harvested. This can majorly influence the lifetime of sensor nodes and in turn that of the sensor network. In our efficient energy harvesting assisted clustering (EEHC) for wireless sensor networks, the effective energy harvesting for wireless sensor networks is experimented and studied through an efficient energy budgeting. The measurement of energy consumption, energy budgeting and energy harvesting are presented as follows.
\nA sensor node consumes energy during sensing the data and forwarding it to the cluster head. A cluster head consumes the energy during the reception, data aggregation and forwarding the aggregated data. The energy consumption of a cluster member to sense and transmit 1-bit of information to the cluster head is estimated in Eq. (3):
\nAssuming a sensing rate of “x,” the total data sensed and transmitted by “n” cluster members in a time period “t” is estimated as given in Eq. (4).
\nSince the maximum number of cluster members are located at 1-hop distance to the cluster head, it is assumed that the data sensed at time “t” is transmitted to the cluster head within the same interval. Suppose a cluster head collects L-bit length of data at time “t,” (i.e., L= x.t.n) then the total energy conservation for data reception, aggregation and forwarding in that CH across time period “t” is estimated as given in Eq. (5).
\nwhere α stands for aggregation ratio.
\nThe total energy consumed in time “t” for a cluster is given in Eq. (6).
\nFor a time slot “t,” the entire cluster, i.e., all the cluster nodes including the cluster head should harvest the energy equal to that of the estimated energy. Suppose there are “n” cluster members and a cluster head, then the energy that is required to be harvested by a sensor node in a cluster is given by the Eq. (7).
\nFor every time interval “T” between time “t1” and “t1+T,” the harvested energy is calculated as given in Eq. (8).
\nIn Eq. (8), the three components represent the energy of the node at starting time “t1,” energy harvested at time interval “T” and energy leakage during this interval. The factor “τ” represents charging efficiency. All the sensor nodes are provided with the storage buffers to store the harvested energy.
\nThe energy consumed must be compensated by the energy harvested within the boundary of a cluster in any given time slot, i.e., the energy budget should harvest more energy than that of the energy consumed in every cluster periodically. An efficient energy budget should ensure that the energy consumption should not be increased than the energy harvested across any time slice.
\nThe performance of our EEHC has been compared with a modern clustering protocol named energy harvesting and information transmission protocol (EHITP) and the classical clustering protocol LEACH under three scenarios. In scenario 1, 100 sensor nodes are deployed in a region of 200 × 200 m2. In scenarios 2 and 3, the population of sensor nodes and area are doubled successively. The experimental results indicate that EEHC exhibits a mean improvement of 91 and 67% when compared to LEACH and EHITP, respectively.
\nThus the harvesting can be made efficient through appropriate budgeting in wireless sensor networks and this budgeting further is influenced by the nature of the sensor applications and critical dynamic constraints.
\nGenerally, wireless sensor networks are employed for two purposes: continuous data monitoring and event monitoring. An example for the former category is a weather monitoring sensor network that measures temperature, moisture, etc. A typical event-based sensor network is habitat monitoring such as surveillance of wild animals and smart home applications. Except a few applications in the second category, most of the senor nodes are put on sleep mode in order to save power and the effective sleep/wakeup scheduling algorithms are required to determine the set of sensor nodes that can be scheduled to sleep with respect to time.
\nThe energy-efficient recursive clustering (EERC) algorithm is an event-driven clustering algorithm, i.e., on the occurrence of an event, the clusters are formed to reduce energy dissemination, in a recursive fashion. The recursive clustering approach employs two stages of clustering process. The first stage of clustering is followed by further partitioning of clusters. Then CHs are elected from energy perspective.
\nThe recursive clustering approach employs two stages of clustering process. After the deployment of sensor nodes, the distance between the nodes is computed using Euclidean distance. Based on the distance, “k” number of clusters are formed which results in the first stage of clusters. Since the clustering process is modeled as recursive process further the “k” number of clusters is divided in “j” number of clusters based on the distance and interval between the nodes which leads to the second stage of clusters. A typical process of this two-stage clustering is pictorially represented as shown in Figure 3. After recursive clustering process, CH is elected based on energy levels and employing round robin scheduling algorithm. Based on the computations, the node with minimum turnaround time and high energy is elected as the CH among the nodes in the cluster.
\nRecursive clustering in wireless sensor networks.
Each node senses the data for every two rounds. After the completion of two rounds, turnaround time is calculated. The node having the minimum turnaround time is elected as the cluster head among the nodes in the cluster. The sensed data from each node is sent to the cluster head. In the cluster head, data is aggregated and sent to the base station by the multi-hop routing. The aggregated data in a cluster head leads to less transmission data, decrease in overheads and decrease in energy consumption.
\nThe performance of our EERC has been evaluated against the conventional LEACH clustering approach under three scenarios through simulation. In scenario 1, the number of sensor node deployed is 100 and in order to test the scalability the scenario 2 and scenario 3 were considered. In scenario 2, 250 sensor nodes and in scenario 3, 500 sensor nodes have been considered.
\nFrom the results obtained for scenario 1, our EERC approach shows performance improvement when compared to the classic LEACH protocol. For 100 nodes, there is 23.85% increase in lifetime, 0.287% decrease in energy consumption, 2.522% decrease in delay, 1.497% increase in transmission time, 1.8% increase in goodput and 0.56% decrease in overhead. In scenario 2, EERC shows performance improvement of 12.58% increase in lifetime, 0.402% decrease in energy consumption, 9.815% decrease in delay, 9.289% increase in transmission time, 0.524% increase in goodput and 0.554% decrease in overhead. In scenario 3, EERC exhibits performance improvement of 11.03% increase in lifetime, 0.619% decrease in energy consumption, 5.735% decrease in delay, 9.289% increase in transmission time, 0.524% increase in goodput and 0.554% decrease in overhead throughput.
\nThus the recursive clustering technique gives considerable improvement across various performance factors, sustaining the equilibrium of the entire network.
\nSimilarity Measure is the metric that is employed in this approach for clustering the sensor nodes from a temporal and spatial perspective. The sensor nodes of the same neighborhood produce similar data. Similarly, the data generated from the same sensor node may exhibit similarity to considerable extend except exceptional scenarios of an application. Also, the data that is generated from the same sensor node on successive timeslots in the period of observation may reveal similarity. The redundancy of the data generation and aggregation is effectively controlled through this technique which considerably contributes to the energy consumption in sensor networks.
\nThe component of similarity measure amidst the data sensed from sensor nodes opens the door to devise an effective sleep schedule and save energy. This idea is capitalized in the proposed adaptive distributed clustering algorithm (ADCA).
\nIt employs two major phases: a cluster formation phase and an adaptive sleep duty cycle phase. In the cluster formation phase, the data generation rate and the similarity between data series are analyzed by the sink. Based on estimation, the nodes are grouped into various clusters. In each cluster, the cluster heads are selected based on the connectivity and residual energy.
\nIn practical scenarios, the clusters may not be in equal size. Based on the similarity measure of the time series, the clustering is done in this approach. By using the similarity measure, the degree of spatial correlation can be calculated. Generally, for two locations with high spatial correlation, their corresponding time series are associated with a high similarity measure. Hence, in a very smooth sub-region, the observed measure has only small changes within the sub-region. In other words, the difference between the observations at any two locations within the sub-region may be very small and hence negligible.
\nTherefore, without compromising the observation reliability, the working sensor nodes within this sub-region could be sparse. On the other hand, the working sensor nodes should be dense in a fast changing sub-region. The spatial sampling rate has to match the spatial variation of the observed physical incident by setting an appropriate similarity measure threshold value. Hence, the similarity measure threshold value includes a degree of independency. This value can be tuned to balance the trade-off between reliability and energy consumption.
\nIn the sleep duty cycle phase, the data generation rates of cluster members are compared with a minimum threshold level. The nodes which have rates lower than the threshold level are cumulatively allotted a sleep duty cycle for a predefined period. The sleep/wakeup schedule is informed to every sensor node. The scheduling is done on a fair and distributed manner to regulate energy consumption.
\nEvery cluster head collects the data from its members and checks for the similarity in the received data. If it encounters a significant level of change, it reports to the sink along with the data. The sink then performs reclustering or rescheduling of sleep duty cycle, if necessary. Thus, a part of the workload of the sink in periodical checking of the nodes is shared by the cluster heads.
\nThe data values of two sensor nodes ni and nj are said to be similar, if
\nwhere mti and mtj are the magnitudes of the values of ni and nj
\nwhere Dij is the distance between ni and nj and DTh is the distance threshold.
\nwhere Ri and Rj are the sending rates of ni and nj, respectively.
\nSending rates are calculated in Eqs. (12) and (13).
\nhere, NPi is the number of packets sent by sensor node i in a time period T.
\nδij is the absolute difference of sending rates of ni and nj
\nδmin is the minimum threshold value for δR.
\nThe two nodes can be represented as points in three dimensions. Node i has a set of coordinates (mti, Di and Ri) and Node j has coordinates (mtj, Dj and Rj).Therefore the Similarity Distance between the nodes ni and nj is given by the Eq. (14).
\nwhere, xi1 = mti, xi2 = Di, xi3 = Ri and xj1 = mtj, xj2 = Dj, xj3 = Rj.
\nHere, “n” refers to the number of similarity metrics.
\nIn our proposed algorithm, the clustering problem is represented as a clique-covering problem. A graph G is created such that each sensor node is a vertex in the graph. An edge (u,v) between nodes u and v is drawn if SM (u,v) > SMTh.
\nA cluster is observed as a clique in this problem. A greedy algorithm is used to heuristically find the cliques. Until all vertices are covered, the search starts from the vertex with the largest node degree. The output of this algorithm is a set of cliques that cover all vertices.
\nThe performance of the proposed ADCA algorithm has been compared against a contemporary clustering algorithm named energy-efficient distributed clustering (EEDC) algorithm [10]. Number of sensor nodes has been varied from 20 to 100 in a simulation area of 500 × 500 m2. The results obtained show the performance improvement gained by ADCA with respect to EEDC in terms of energy consumption (25%), delay (18%) and delivery ratio (20%) from the mean measurements of multiple runs.
\nThus the effective sleep duty cycle considerably reduces the energy consumption in wireless sensor networks ensuring that the overhead and delay are not increased under such scenarios.
\nWireless Sensor networks are more sophisticated in their requirements and to provide clustering solutions for them requires adequate knowledge on the nature of the applications, capacity limitations of sensor nodes, the tradeoff among the expected performance parameters and the limitations of emerging technologies.
\nThis chapter has outlined four modern clustering approaches (EAFCA, EEHC, EERC and ADCA) designed for wireless sensor networks, each from a distinguishable perspective. The simulation results obtained for the proposed clustering approaches are encouraging. This will kindle researchers to explore further in this area. The journey of research in this field has crossed significant milestones, yet it has been left with many open-ended issues and unexplored roads due to the presence of inherent trade-offs among the performance factors and dynamic needs of sensor applications. The performance of a wireless sensor network can further be explored through holistic approaches invented or inherited from modern technological advancements.
\nThere may not be a precise background to the first discovery and application of phase change materials (PCMs). Perhaps, from the earliest days where human has acquired the intellect, he has realized the existence of these substances or, maybe, has used them without recognizing their nature. Throughout science and technology evolution, more precisely, since the heat capacity of materials and sensible or latent heats have been known, their ability to store and release thermal energy has also been considered. However, A. T. Waterman submitted the first report of discovery in the early 1900s. In recent years, scientists have paid particular attention to these materials, and their commercialization began from those years.
Perhaps the main reason for this attention was the problems caused by energy mismanagement and improper use of it. Today, inadequate energy management, especially fossil fuels, has caused many environmental and economic problems. Therefore, the necessity of efficient energy demand as well as development of renewable energies and energy storage systems is highly significant. One of the important topics in this field is the design of special energy storage equipment to other types. Energy storage not only reduces the discrepancy between energy supply and demand but also indirectly improves the performance of energy generation systems as well as plays a vital role in saving of energy by converting it into other reliable forms. Hence, this matter saves high-quality fuels and reduces energy wastes [1, 2, 3].
Energy storage is one of the important parts of renewable energies. Energy can be stored in several ways such as mechanical (e.g., compressed air, flywheel, etc.), electrical (e.g., double-layer capacitors), electrochemical (e.g., batteries), chemical (e.g., fuels), and thermal energy storages [4].
Among several methods of energy storage, thermal energy storage (TES) is very crucial due to its advantages. TES is accomplished by changing the internal energy of materials, such as sensible heat, chemical heat, latent heat, or a combination of them.
In sensible heat storage (SHS) systems, heat can be stored by increasing the temperature of a material. Hence, this system exploits both the temperature changes and the heat capacity of the material to store energy. The amount of heat stored in this system depends on the specific heat, temperature differences, and amount of material; thus it requires a large amount of materials, whereas Latent heat storage (LHS) is generally based on the amount of heat absorbed or released during the phase transformation of a material. Lastly, In the chemical heat storage (CHS), heat is stored by enthalpy change of a chemical reaction.
Among the aforementioned heat storage systems, the LHS is particularly noteworthy. One of the special reasons is its ability to store large amount of energy at an isothermal process [5, 6, 7].
Any high-performance LHS system should contain at least one of the following terms:
Appropriate PCM with optimum melting temperature range
Desirable and sufficient surface area proportional to the amount of heat exchange
Optimal capacity compatible with PCM
Phase change materials perform energy storage in LHS method. In this case, a material during the phase change absorbs thermal energy from surrounding to change its state, and in the reverse process, the stored energy is released to the surrounding. PCMs initially behave likewise to other conventional materials as the temperature increases, but energy is absorbed when the material receives heat at higher temperatures and close to the phase transformation. Unlike conventional materials, in PCMs absorption or release of thermal energy is performed at a constant temperature. A PCM normally absorbs and releases thermal energy 5–14 times more than other storage materials such as water or rock [8, 9].
PCMs can store thermal energy in one of the following phase transformation methods: solid-solid, solid-liquid, solid-gas, and liquid-gas. In the solid-solid phase change, a certain solid material absorbs heat by changing a crystalline, semicrystalline, or amorphous structure to another solid structure and vice versa [10]. This type of phase change, usually called phase transitions, generally has less latent heat and smaller volume change comparing to the other types. Recently, this type of PCM has been used in nonvolatile memories [11].
Solid-liquid phase change is a common type of commercial PCMs. This type of PCM absorbs thermal energy to change its crystalline molecular arrangement to a disordered one when the temperature reaches the melting point. Unlike solid-solid, solid-liquid PCMs contain higher latent heat and sensible volumetric change. Solid-gas and liquid-gas phase changes contain higher latent heat, but their phase changes are associated with large volumetric changes, which cause many problems in TES systems [8]. Although the latent heat of solid-liquid is less than liquid-gas, their volumetric change is much lower (about 10% or less). Therefore, employing PCMs based on solid-liquid phase change in TES systems would be more economically feasible.
The overall classification of energy storage systems as well as phase change materials is given in Figure 1.
Overview of energy storage and classification of phase change materials.
As mentioned in the previous section, despite the high thermal energy absorption capacity, PCMs in liquid-gas and solid-gas transitions have extremely high volume changes. On the other hand, solid-solid PCMs also have a lower thermal energy storage capacity. Therefore, the abovementioned PCMs, with the exception of specific cases, have not received much attention to commercialization. Currently, the most common type of transition that has been mass-marketed is solid-liquid PCMs. The classification of phase change materials is schematically given in Figure 1. Solid-liquid PCMs are generally classified as three general organics, inorganic, and eutectics [12, 13]. However, in some references they are classified into two major organics and inorganics.
Inorganic PCMs mainly have high capacity for thermal energy storage (about twice as much as organic PCMs) as well as have higher thermal conductivity. They are often classified as salt hydrates and metals.
Salt hydrates are the most important group of inorganic PCMs, which is widely employed for the latent heat energy storage systems. Salt hydrates are described as a mixture of inorganic salts and water (AB × nH2O). The phase change in salt hydrates actually involves the loss of all or plenty of their water, which is roughly equivalent to the thermodynamic process of melting in other materials.
At the phase transition, the hydrate crystals are subdivided into anhydrous (or less aqueous) salt and water. Although salt hydrates have several advantages, some deficiencies make restrictions in their application. One of these problems is incongruent melting behavior of salt hydrates. In this problem the released water from dehydration process is not sufficient for the complete dissolution of the salts. In this case, the salts precipitate and as a result phase separation occurs. In order to prevent this problem, an additional material such as thickener agent is added to salt hydrates. Another major problem with salt hydrates is the supercooling phenomenon. In this phenomenon, when crystallization process occurs, the nucleus formation is delayed; therefore, even at temperatures below freezing, the material remains liquid [7, 11, 14].
Overall, the most attractive properties of salt hydrate are (i) high alloy latent temperature, (ii) relatively high thermal conductivity (almost two to five times more than paraffin), and (iii) small volume changes in melting. They are also very low emitting and toxic, adaptable to plastic packaging, and cheap enough to use [15].
Metalsare another part of the inorganic PCMs. Perhaps the most prominent advantages of metals are their high thermal conductivity and high mechanical properties. Metals are available over a wide range of melting temperatures. They are also used as high-temperature PCMs.
Some metals such as indium, cesium, gallium, etc. are used for low-temperature PCMs, while others such as Zn, Mg, Al, etc. are used for high temperatures. Some metal alloys with high melting points (in the range of 400–1000°C) have been used for extremely high temperature systems. These metal alloys as high-temperature PCMs can be used in the field of solar power systems [16, 17]. They can also be used in industries that require temperature regulation in furnaces or reactors with high operating temperatures.
Perhaps the most important fragment is the organic PCMs. Organic PCMs show no change in performance or structure (e.g., phase separation) over numerous phase change cycles. In addition, supercooling phenomena cannot be observed in organic PCMs. The classification of organic PCMs is unique. This division is mainly based on their application contexts. In general, they are classified into two major paraffin and non-paraffin sections.
Paraffins are the most common PCMs. Since this book is about paraffin, to avoid duplication, this section will briefly discuss the chemistry (structure and properties) of paraffin, but their ability as phase change materials will be reviewed in detail.
Non-paraffinic organic PCMs are known to be the most widely used families. In addition to their different properties compared to paraffins, they have very similar properties to each other. Researchers have used various types of ether, fatty acid, alcohol, and glycol as thermal energy storage materials. These materials are generally flammable and less resistant to oxidation [18, 19, 20].
Although non-paraffin organic PCMs have high latent heat capacity, they have weaknesses such as flammability, low thermal conductivity, low combustion temperatures, and transient toxicity. The most important non-paraffinic PCMs are fatty acids, glycols, polyalcohols, and sugar alcohols.
Fatty acids [CH3(CH2)2nCOOH] also have high latent heat. They can be used in combination with paraffin. Fatty acids exhibit high stability to deformation and phase separations for many cycles and also crystallize without supercooling. Their main disadvantages are their costs. They are 2–2.5 times more expensive than technical grade paraffins. Unlike paraffins, fatty acids are of animal or plant origin. Their properties are similar to those of paraffins, but the melting process is slower. On the other hand, they are moderately corrosive as well as generally odorous [21].
A eutectic contains at least two types of phase change materials. Eutectics have exceptional properties. In eutectics, the melting-solidification temperatures are generally lower than the constituents and do not separate into the components through the phase change. Therefore, phase separation and supercooling phenomena are not observed in these materials.
Eutectics typically have a high thermal cycle than salt hydrates. Inorganic-inorganic eutectics are the most common type of them. However, in recent studies, organic-inorganic and organic-organic varieties have received more attention. The major problem of eutectics is their commercialization. Their cost is usually two to three times higher than commercial PCMs [22, 23].
Some of the above PCMs and their thermal properties, which are competitive with paraffins in terms of latent heat capacity, are summarized in Table 1.
Type of PCMs | Materials | Melting point (°C) | Latent heat (kJ/kg) | Density* (kg/m3) | Thermal conductivity (W/mK)** | Ref. | |
---|---|---|---|---|---|---|---|
Inorganic salt hydrates | LiClO3·3H2O | 8 | 253 | 1720 | [24, 25] | ||
K2HPO4·6H2O | 14 | 109 | [24] | ||||
Mn(NO3)2·6H2O | 25.8 | 126 | 1600 | [14, 25] | |||
CaCl2·6H2O | 29.8 | 191 | 1802 | 1.08 | [24, 25] | ||
Na2CO3·10H2O | 32–34 | 246–267 | [14, 24] | ||||
Na2SO4·10H2O | 32.4 | 248, 254 | 1490 | 0.544 | [14, 26] | ||
Na2HPO4·12H2O | 34–35 | 280 | 1522 | 0.514 | [15, 26] | ||
FeCl3·6H2O | 36–37 | 200, 226 | 1820 | [25, 26] | |||
Na2S2O3·5H2O | 48–49 | 200, 220 | 1600 | 1.46 | [15, 26] | ||
CH3COONa·3H2O | 58 | 226, 265 | 1450 | 1.97 | [15, 26] | ||
Non-paraffinic organic PCMs | Fatty acids | Formic acid | 8.3 | 247 | 1220 | — | [1, 25] |
n-Octanoic acid | 16 | 149 | 910 | 0.148 | [21, 27] | ||
Lauric acid | 43.6 | 184.4 | 867 | [21, 25] | |||
Palmitic acid | 61.3 | 198 | 989 | 0.162 | [21, 27] | ||
Stearic acid | 66.8 | 259 | 965 | 0.172 | [21, 25] | ||
Polyalcohols | Glycerin | 18 | 199 | 1250 | 0.285 | [1, 25] | |
PEG E600 | 22 | 127.2 | 1126 | 0.189 | [27] | ||
PEG E6000 | 66 | 190 | 1212 | [27] | |||
Xylitol | 95 | 236 | 1520 | 0.40 | [28] | ||
Erythritol | 119 | 338 | 1361 | 0.38 | [28] | ||
Others | 2-Pentadecanone | 39 | 241 | [1, 25] | |||
4-Heptadekanon | 41 | 197 | [1, 25] | ||||
D-Lactic acid | 52–54 | 126, 185 | 1220 | [1, 25] | |||
Eutectics | O-O, O-I, I-I *** | CaCl2·6H2O + MgCl2·6H2O | 25 | 127 | 1590 | [27] | |
Mg(NO3)2·6H2O + MgCl2·6H2O | 59 | 144 | 1630 | 0.51 | [27] | ||
Trimethylolethane + urea | 29.8 | 218 | [21] | ||||
CH3COONa·3H2O + Urea (60:40) | 31 | 226 | [27] | ||||
Metals | Mg-Zn (72:28) | 342 | 155 | 2850 | 67 | [16, 17] | |
Al-Mg-Zn (60:34:6) | 450 | 329 | 2380 | [16, 17] | |||
Al-Cu (82:18) | 550 | 318 | 3170 | [16, 17] | |||
Al-Si (87.8:12.2) | 580 | 499 | 2620 | [16, 17] |
Thermophysical properties of some common PCMs with high latent heat.
At 20°C.
Just above melting point (liquid phase).
Inorganic-inorganic (I-I), organic-inorganic (O-I), and organic-organic (O-O).
Paraffin is usually a mixture of straight-chain n-alkanes with the general formula CH3-(CH2)n-CH3. However, in some cases, paraffin is used as another name for alkanes. Gulfam R. et al. in their article have classified paraffins based on the number of carbon atoms as well as their physical states. According to this classification, at room temperature, 1–4 numbers of carbons refer to pure alkanes in a gas phase, 5–17 carbons are liquid paraffins, and more than 17 is known as solid waxes. These waxy solids refer to a mixture of saturated hydrocarbons such as linear, iso, high branched, and cycloalkanes [29]. Generally, paraffin-based PCMs are known as waxy solid paraffins. Commercial paraffins contain mixture of isomers, and therefore, they have a range of melting temperatures.
Paraffins typically have high latent heat capacity. If the length of the chain increases, the melting ranges of waxes also increase, while the latent heat capacity of melting is not subject to any particular order (Table 2).
Materials | Melting point (°C) | Latent heat (kJ/kg) | Density* (kg/m3) | Thermal conductivity** (W/mK) |
---|---|---|---|---|
n-Tetradecane (C14) | 6 | 228–230 | 763 | 0.14 |
n-Pentadecane (C15) | 10 | 205 | 770 | 0.2 |
n-Hexadecane (C16) | 18 | 237 | 770 | 0.2 |
n-Heptadecane (C17) | 22 | 213 | 760 | 0145 |
n-Octadecane (C18) | 28 | 245 | 865 | 0.148 |
n-Nonadecane (C19) | 32 | 222 | 830 | 0.22 |
n-Eicosane (C20) | 37 | 246 | ||
n-Henicosane (C21) | 40 | 200, 213 | 778 | |
n-Docosane (C22) | 44.5 | 249 | 880 | 0.2 |
n-Tricosane (C23) | 47.5 | 232 | ||
n-Tetracosane (C24) | 52 | 255 | ||
n-Pentacosane (C25) | 54 | 238 | ||
n-Hexacosane (C26) | 56.5 | 256 | ||
n-Heptacosane (C27) | 59 | 236 | ||
n-Octacosane (C28) | 64.5 | 253 | ||
n-Nonacosane (C29) | 65 | 240 | ||
n-Triacontane (C30) | 66 | 251 | ||
n-Hentriacontane (C31) | 67 | 242 | ||
n-Dotriacontane (C32) | 69 | 170 | ||
n-Triatriacontane (C33) | 71 | 268 | 880 | 0.2 |
Paraffin C16-C18 | 20–22 | 152 | ||
Paraffin C13-C24 | 22–24 | 189 | 900 | 0.21 |
RT 35 HC | 35 | 240 | 880 | 0.2 |
Paraffin C16-C28 | 42–44 | 189 | 910 | |
Paraffin C20-C33 | 48–50 | 189 | 912 | |
Paraffin C22-C45 | 58–60 | 189 | 920 | 0.2 |
Paraffin C21-C50 | 66–68 | 189 | 930 | |
RT 70 HC | 69–71 | 260 | 880 | 0.2 |
Paraffin natural wax 811 | 82–86 | 85 | 0.72 (solid) | |
Paraffin natural wax 106 | 101–108 | 80 | 0.65 (solid) |
In general, paraffin waxes are safe, reliable, inexpensive, and non-irritating substances, relatively obtained in a wide range of temperatures. As far as economic issues are concerned, most technical grade waxes can be used as PCMs in latent heat storage systems. From the chemical point of view, paraffin waxes are inactive and stable. They exhibit moderate volume changes (10–20%) during melting but have low vapor pressure.
The paraffin-based PCMs usually have high stability for very long crystallization-melting cycles. Table 2 illustrates the thermal properties of some paraffin waxes.
Besides the favorable properties, paraffins also show some undesirable properties such as low thermal conductivity, low melting temperatures, and moderate-high flammability. Some of these disadvantages especially thermal conductivity and flammability can be partially eliminated with the help of additives or paraffin composites.
Measures must be taken to make the solid-liquid PCMs usable. For this purpose, there are several methods for stabilizing the shapes of paraffinic PCMs. Two main methods of them are discussed below.
Encapsulation is generally a worthy method to protect and prevent leakage of PCMs in the liquid state. The capsules consist of two parts, the shell and the core. The core part contains PCMs, whereas the shell part is usually composed of polymeric materials with improved mechanical and thermal properties. The shell part plays the role of protection, heat transfer, and sometimes preventing the release of toxic materials into the environment. In these cases, the shell must have appropriate thermal conductivity. Polymeric shells are also commonly used in encapsulating PPCMs. The choice of core part depends on its application field. The encapsulation of PPCMs is classified into three major parts: bulk or macroencapsulation, microencapsulation, and nano-encapsulation.
Macroencapsulation is one of the simplest ways to encapsulate paraffins. This method has a lower cost than other methods. These products are used in transportation, buildings, solar energy storage systems, and heat exchangers. Sometimes metals are also used as shell materials [30].
In order to increase the efficiency of heat transfer in these types of capsules, either the size of the capsules should be appropriately selected or suitable modifiers should be used. In general, the smaller the diameter of spherical capsules or cylinders, the better the heat transfer. In some cases, metal foams are used to improve the heat transfer properties of paraffin. Aluminum and copper open-cell foams are among the most studied, whereas, in other cases metal oxides, metals and graphite are used [30, 31].
There are various forms of macroencapsulation, such as ball shape, spherical shape, cylindrical, flat sheets, tubular, etc. [31]. Cylindrical tubes are one of the famous forms of macroencapsulated PPCMs. This type of encapsulation is most commonly used in buildings or in solar energy storage systems.
Most of the research carried out on macroencapsulated PPCMs has been focused on improving their thermal conductivity. In one of these studies, different metal oxide nanoparticles such as aluminum oxide, titanium oxide, silicon oxide, and zinc oxide were used to improve the thermal conductivity of paraffin. The results show that titanium oxide performs better under the same conditions than the other oxides [32]. In a similar study, copper oxide nanoparticles were used to improve thermal conductivity and performance of paraffin in solar energy storage systems [33]. In some studies, graphite flakes and expanded graphite have also been used as improving agent for heat conductivity [31].
Hong et al. have used polyethylene terephthalate pipes as a shell for paraffin. In this macroencapsulated system, introduced as cylinder modules, float stone has been added to paraffin as an enhancer of thermal conductivity. In this study, the effect of various parameters such as pipe diameter on heat transfer is investigated, and the results of experimental section are compared with modeling [34].
D. Etansova et al. studied numerical computation and heat transfer modeling of paraffin-embedded stainless steel macroencapsulates for use in solar energy storage systems. In this study, the effect of geometric size and shape on heat transfer was investigated [35].
Microencapsulation of PCMs is another suitable way to improve efficiency and increase thermal conductivity. The size of the microencapsulates usually ranges from 1 μm to 1 mm. Microencapsulation of paraffins is a relatively difficult process, but it performs better than macroencapsulates. This is due to increased contact surface area, shorter discharge and loading times, and improved thermal conductivity. Different materials are used for the shell part of the microencapsulates.
In general, there are two major physical and chemical methods for microencapsulation. The most important physical methods are fluidized bed, spray dryer, centrifuge extruder, and similar processes. However, chemical methods are often based on polymerization. The most important techniques include in situ suspension and emulsion polymerization, interfacial condensation polymerization, and sol-gel method. The latter is sometimes known as the physicochemical method [12, 29].
In the suspension or emulsion polymerization method, the insoluble paraffin is first emulsified or suspended in a polar medium, which is predominantly aqueous phase, by means of high-speed stirring. Surfactants are used to stabilize the particles. Then, lipophilic monomers are added to the medium, and the conditions are prepared for polymerization. This polymer, which is insoluble in both aqueous and paraffin phases, is formed on the outer surface of paraffin particles and finally, after polymerization, encapsulates the paraffin as a shell. The size of these capsules depends on the size of emulsion or suspension of paraffin droplets. Sometimes certain additives are added to the medium to improve some of the polymer properties. For instance, in some studies, polyvinyl alcohol (PVA) has been added to the medium with methyl-methacrylate monomer, which is known as one of the most important shell materials. As a result, paraffin has been encapsulated by PVA modified polymethyl methacrylate (PMMA). Adding this modifier forms a smooth surface of the microencapsulates [36, 37].
In the interfacial method, soluble monomers in the organic phase with other monomers in the aqueous phase at the droplet interface form a polymer that precipitates on the outer layer of the organic phase.
The sol-gel method is a multi-step procedure. In this method, firstly, an organosilicon compound such as tetraethoxysilane (TEOS) is hydrolyzed in an acidic medium at low pH. The prepared homogenous solution is known as the sol part. Then, the paraffin emulsion is prepared in an aqueous medium and stabilized by special emulsifiers. Actually, these emulsifiers are the first layer of the shell. Subsequently, the sol solution is slowly added to the aqueous phase containing paraffin. The silicon compounds containing OH groups (silanols) form hydrogen bonding with polar side of emulsifiers, and finally the condensation process is carried out on the first layer interface. As a result, paraffin microencapsulates with an inorganic material that is often silica. Silica is one of the significant materials used as a shell for micro and nano-encapsulation. Silica has high thermal conductivity and on the other hand has better mechanical properties than some polymers [38, 39, 40, 41].
As mentioned, most of the materials used to microencapsulation are polymers. The main polymers used as shell materials are polymethyl methacrylate [42], polystyrene [43], urea-formaldehyde [44], urea-melamine-formaldehyde [45], polyaniline [46], etc. However, in many cases, these polymers are used in modified form. For example, polymethyl methacrylate modified with polyvinyl alcohol or with other methacrylates [36, 37], polystyrene copolymers [47], and melamine modified-formaldehyde with methanol [48] can be considered. Table 3 shows the most common polymers used as shell materials.
Core material PPCM | Shell material | Encapsulation method | Particle size (μm) | Recommended application | Ref |
---|---|---|---|---|---|
n-Nonadecane | Polymethyl methacrylate | Emulsion | ~ 8 | Smart building and textiles | [42] |
n-Heptadecane | Polystyrene | Emulsion | <2 | General fields | [43] |
Commercial paraffin wax | Polystyrene-co-PMMA | Suspension | ~ 20 | [50] | |
Commercial RT21 | PMMA | Suspension | 20–40 | [36] | |
Commercial RT21 | PMMA modified with PVA | Emulsion | 15 | Building | [37] |
Commercial paraffin wax | Polyaniline | Emulsion | <1 | [46] | |
Commercial paraffin wax | Urea-formaldehyde | In situ | ~ 20 | [44] | |
n-Octadecane, n-nonadecane | Urea-melamine-formaldehyde | In situ | 0.3-0.6 | [45] | |
Commercial paraffin wax | Methanol-melamine-formaldehyde | In situ | 10–30 | Building | [48] |
Commercial paraffin wax | Silica | Sol-gel | 4–10 | Textile | [38] |
Commercial paraffin wax | Silica | Sol-gel | 0.2–0.5 | [39] | |
n-Octadecane | Silica | Sol-gel | 7–16 | [40] | |
n-Pentadecane | Silica | Sol-gel | 4–8 | [41] |
Common materials for microencapsulation of PPCMs.
In addition to the aforementioned microencapsulation approaches, which mainly form polymeric materials as shells, other materials have been also recommended. For example, Singh and colleagues have used silver metal as a shell for paraffin microencapsulates. They first emulsified paraffin into small particles in water and then converted silver salts to metallic silver via an in situ reduction reaction. The average particle size of 329 μm has been reported, and the thermal properties of paraffin have been investigated using DSC and TGA. This type of metal shell microencapsulates has been suggested for use in microelectronics heat management systems [49].
There are several techniques to study the properties of micro and nano-encapsulates. In all studies, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) have been used to determine the thermal properties of PPCMs, such as enthalpy of fusion, melting temperature, weight loss, degradation, etc. Various methods such as XRD, FTIR, and 12C NMR have been used to study the structure and chemical composition of PPCMs. The morphology and diameters of the microcapsules have often been studied by scanning electron microscopy (SEM) and particle size analyzer.
The latter technique is used to study the influence of different variables on the diameter of the microcapsules. One of these variables is the effect of stirring speed on emulsification of paraffin. The results of some studies show that higher stirring speed of emulsification process leads to decrease of the mean size of paraffin droplets [48].
Along with studies on the type of microcapsules, many studies have been conducted to improve thermal conductivity and mechanical properties of microencapsulates. Part of these studies has been dedicated to the effect of graphene and graphene oxide on the improvement of thermal conductivity [51]. L. Zhang et al. investigated the effect of graphene oxide on improving the mechanical properties and leakage protection as well as improving the thermal conductivity of melamine-formaldehyde as shell materials of PPCM microencapsulates [52]. In another part of studies, metals and metal oxides have been used. For example, 10 and 20 wt% of nanomagnetite (Fe3O4) with particle size from 40 to 75 nm increase the thermal conductivity by 48 and 60%, respectively [53]. Also, addition of TiO2 and Al2O3 nanoparticles in a mass fraction of 5% with respect to PPCM at the size range of 30–60 nm increases the thermal conductivity by 40 and 65%, respectively [54].
Nano-encapsulation of PPCM is very similar to the microencapsulation process. However, these types of encapsulation specific techniques, such as ultrasonic, are used to adjust the size of the paraffin droplets to less than 1 micron. In the next step, using the chemical methods mentioned in the microencapsulation method, the shell formation is performed. The most common method for nano-encapsulation is the emulsion polymerization method. However, although limited, interfacial and sol-gel methods have also been reported.
In recent years, research on polymeric matrix-based shape-stable PCMs has gained great importance. Among these types of phase change materials, the paraffin-polymer composite is particularly attractive. The combination of paraffin and polymers as new PCMs with a unique controllable structure can be widely used. This compound remains solid at paraffin melting point and even above without any softening, which is why this type of PCM is called shape-stable. These materials are well formed and have high-energy absorption capacity; hence they can be widely used as stable PCMs with specific properties. On the other hand, some problems such as high cost and difficulty of encapsulating processes could be resolved. Despite these advantages, some common disadvantages such as low thermal stability, low thermal conductivity, and relatively high flammability can restrict their application, particularly in building materials. For this reason, further studies are required to eliminate these disadvantages and improve the properties of these materials. A large part of research is relevant to increase or improve their thermal conductivity, flame retardation, and thermophysical and mechanical properties. Suitable additives are proposed to improve these properties [55, 56].
In some articles, a simple method involves mixing-melting of polyethylene and paraffin, consequently cooling the composite, or using a simple twin extruder to prepare a shape-stable PCM has been reported [57, 58]. When this compound contains sufficient polymer, a homogeneous mixture remains solid at temperatures above the melting point of paraffin and below the polymer melting point. During the preparation of these composites, no chemical reaction or chemical bonds are formed between the polymers and paraffin; therefore these types of compounds are considered as physical mixtures. Shape-stable PPCMs can be used in all previously described areas. Due to the thermoplastic properties of these composites, it is possible to melt and crystalize them for many cycle numbers. Shape-stable PPCMs have several advantages over other PCMs. They are also nontoxic and do not require high-energy consumption during production process.
Inaba and Tu [59] developed a new type of shape-stable PPCM and determined their thermophysical properties. These materials can be used without encapsulation. Feldman et al. [60] prepared plates of shape-stable PCM and determined their high thermal energy storage capacity when used in small chambers. In this type of polymer-based plates, fatty acids are used as PCMs that absorb or releases large amounts of heat during melting and solidification, without altering the composition of the shape-stable PCM. The same researchers determined the role of polymer-PCM sheets in stabilizing the shape and size of the plates when PCM was liquefied. The composition of paraffin and high-density polyethylene (HDPE) has been studied by Lee and Choi [61] and has been introduced as a shape-stable energy storage material. In this study, the amount of energy stored by the mentioned composites is also studied. They also studied the morphology of the high-density polyethylene crystal lattice (HDPE) and its effect on paraffin through scanning electron microscopy and optical microscopy (OM) analysis. On the other hand, they also reported of high thermal energy storage capacity of the prepared paraffin/HDPE-based shape-stable PCMs. Hong and Xin-Shi [62] synthesized polyethylene-paraffin as a shape-stable PCM and characterized its morphology and structure by scanning electron microscopy and its latent heat of melting by differential scanning calorimetry. In this study, a composition consisting of 75% paraffin as a cheap, effective, easy-to-prepare, low-temperature shape-stable PPCM is recommended. In another study, Xiao et al. [63] prepared a shape-stable PCM based on the composition of paraffin with a thermoplastic elastomer (styrene butadiene rubber) and determined its thermal properties. The obtained results show that the stable mixture has the phase changing property and the amount of latent heat of melting stored in this compound is estimated to be 80% of pure paraffin. In another part of this study, the thermal conductivity of PCMs was significantly increased by using graphite.
Despite the above benefits, some disadvantages of shape-stable PPCMs are also reported. One of the major problems is the softening and paraffin leakage phenomenon at elevated temperatures. Seiler partly resolved this problem by adding a different ratio of silica and copolymers to the polyethylene-paraffin composition [64]. Another problem is the low thermal conductivity of the polyethylene-paraffin compound. A lot of research has been conducted to increase this property. A. Sari [65] prepared two types of paraffin with different melting temperatures (42–44°C and 56–58°C) and combined each with HDPE as phase modifier. By addition of 3% expanded graphite, the thermal conductivity of composites increased by 14 and 24%, respectively. Zhang et al. [66] developed new PCMS based on graphite and paraffin with high thermal energy storage capacity and high thermal conductivity. Zhang and Ding et al. [67] have used various additives such as diatomite, Wollastonite, organic modified bentonite, calcium carbonate, and graphite to improve the thermal conductivity of shape-stable PCMs.
It should be noted that metal particles and metal oxides due to their higher thermal conductivity are widely used to improve this property of PCMs. One of the materials that has received more attention in recent years is alumina. Aluminum oxide nanoparticles were added to paraffin to increase its thermal conductivity in both liquid and solid states [57, 68]. This compound coupled with its high thermal conductivity is cheaper and more abundant than other metal oxides.
Another problem with shape-stable PPCMs is their flammability. The effect of various additives has been studied by scientists to eliminate this problem. One of the most effective of these substances is halogenated compounds, but they cause environmental pollution and also release toxic compounds while burning. Researchers have used hybrid and environmentally friendly materials to enhance the durability of flame retardant materials. They studied the effect of clay nanoparticles and organo-modified montmorillonite. Adding these materials not only increases their resistance to burning but also increases their mechanical and thermal properties [69, 70, 71]. In another study, Y. Cai et al. added paraffin, HDPE, and graphite, then added ammonium polyphosphate and zinc borate separately, and studied their resistance to burning. The results show that the addition of ammonium polyphosphate decreases flammability, while zinc borate increases the flammability risk [72]. One of the most interesting and harmless fire retardant compounds is metal hydroxides, especially aluminum hydroxide, magnesium hydroxide, or their combination [73, 74, 75].
Some researchers have used other advanced materials as supporting materials to prepare shape-stable PPCMs instead of using the polymer matrix [76, 77, 78]. Rawi et al. used acid-treated multi-walled carbon nanotubes (A-CNT). They reported that adding 5% by weight A-CNT to paraffin decreases 25% of the latent heat while increasing heat conductivity up to 84% [79]. Y. Wan et al. used pinecone biochar as the supporting matrix for PCMs. They prepared shape-stable PCM materials at different ratios and studied the leakage behavior. The optimal ratio is suggested as 60% of the PCM. For the above ratio, no PCM leakage was observed after the melting temperature. The results showed that the thermal conductivity of the same ratio shape-stable PCM increased by 44% compared to the pure PCM [80].
PCMs are available in a wide range of desired temperature ranges. Obviously, a PCM may not have all the properties required to store heat energy as an ideal material. Therefore, it would be more appropriate to use these materials in combination with either other PCMs or various additives to achieve the required features. However, as latent heat storage materials, while using PCMs, the thermodynamic, kinetic, and chemical properties as well as the economic and availability issues of them must be taken into account. Employed PCMs must have the optimum phase change temperature. On the other hand, the higher the latent heat of the material, the lower its physical size. High thermal conductivity also helps to save and release energy. From the physical and kinetic point of view, the phase stability of PCMs during melting and crystallization contributes to optimum thermal energy storage. Their high density also enables high storage at smaller material sizes. During phase change, smaller volume changes and lower vapor pressures are appropriate for continuous applications.
H. Nazir et al. in their review article [12] have explained the criteria for selection of PCMs as a pyramid. In this pyramid, at the bottom, known as the fundamentals, there are several items such as cost, regularity compliance, and safety. In the next section, the thermophysical properties such as energy storage capacity and runtime are discussed. In the upper section, reliability and operating environment consist of degradation, cycle life, shelf life, and thermal limits are reflected. Finally, at the top section of pyramid, user perception and convenience are located. These criteria help us to find a proper PCM for certain application fields.
These criteria may also be extended to paraffinic PCMs. Nowadays, paraffinic PCMs (PPCMs) are widely used as thermal energy storage materials, including solar energy storage systems, food industries, medical fields, electrical equipment protection, vehicles, buildings, automotive industries, etc. [24, 29, 81, 82, 83, 84, 85].
Generally, application fields of PPCMs can be considered in two main sections: thermal protection and energy storage purposes. The major difference between these two areas of application is in thermal conductivity of the PPCMs.
Protection and transportation of temperature-sensitive materials is one the mentioned area. Sometimes a certain temperature is required to transport sensitive medicines, medical equipment, food, etc. In all cases, using of PPCMs would be appropriate as they can regulate and stabilize the temperature over a given range. Similarly, in sensitive electrical equipment, these materials are also essential to prevent the maximum operating temperature. On the other hand, they can be used to prevent possible engine damage at high temperatures [86, 87].
One of the studies related to these issues is the use of paraffin containing heavy alkanes to protect electronic devices against overheating. In this study, paraffin has been used as a protective coating for the resistor chip, and its effect on cooling of the devices has been investigated. Experimental results show that paraffin coating increases the relative duration of overheating by 50 to 150% over the temperature range of 110–140°C [88]. In another study, a mixture of paraffin and polypropylene has been used as an overheating protector in solar thermal collectors [89].
However, energy storage purposes are the most important part of PPCM application. In general, PCMs act as passive elements and therefore do not require any additional energy source. Most studies on the application of energy storage properties of PPCMs have been confined to buildings, textiles, and solar systems. In the following, building applications will be further attended.
One of the main drawbacks of lightweight building materials is their low thermal storage capacity, which results in extensive temperature fluctuations as a result of intense heating and cooling. Therefore, PPCMs have been used in buildings due to their ability to regulate and stabilize indoor temperatures at higher or lower outdoor temperatures [90].
Generally, PPCMs in buildings are used as thermal energy storage at daytime peak temperature, and they released the stored energy at night when temperatures are low. The result of this application is to set the comfort condition for a circadian period. This application minimizes the amount of energy consumed for cooling during the day and warming up at night.
In contrast, in order to stabilize the ambient conditions at low temperatures, some special PCMs are also used in air conditioner systems. In this case, cool air is stored during the night and released into the warm hours of the day.
Y. Cui et al. [91] in a review article categorized PPCM application methods based on their location of use such as PCMs in walls, floor heating systems, ceiling boards, air-based solar heating systems, free cooling systems (with ventilation systems), and PCM shutter (in windows). Both types of encapsulation and shape-stable PPCMs could be used in all of the above classification of building applications. Sometimes these materials can be added directly to concrete, gypsum, etc. [90, 92, 93, 94, 95].
In order to increase the performance of PPCMs in this application field, great deals of studies have also been done on improving their thermal conductivity. On the other hand, extensive research into safety issues has been done to reduce the flammability of PPCMs by adding flame retardants to these materials.
Overall, these studies cover the importance of using PPCMs in heating and cooling as well as indicate the general characteristics, advantages, and disadvantages of these materials used for thermal storage in buildings.
It is clear that at this time, where renewable energy is particularly important, the use of PPCMs is on the rise. As it has been mentioned, PPCMs have many application fields due to their advantages. For example, they can be used in the construction, pharmaceutical and medical industries, textiles, automobiles, solar power systems, transportation, thermal batteries, heat exchangers, and so on.
This chapter of the book has attempted to focus more on how to use paraffins. For this reason, two methods, namely, encapsulation and shape-constant, have been widely discussed. In addition, improving their weak properties such as thermal conductivity and flammability has also been studied. Depending on the benefits of paraffins, new applications are suggested every day. Extensive studies are underway on other new applications in recent years.
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