*2.1. System Design*

The electrical schematic and the specifications of the key components of the e-bike charging station are shown in Figure 3 and Table 1, respectively. The core of the system is a 48 V DC nano-grid, which supports the power exchange between all the components. The PV generation consists of eight modules arranged in four parallel strings. A Victron BlueSolar 150/85 maximum power point tracking (MPPT) converter is used to process power from the PV panels to charge the 48 V battery bank [38]. Isolated DC-DC converters and a high-frequency DC/AC inverter are connected to the 48 V DC nano-grid for DC and wireless charging of e-bikes, respectively.

The connection between the DC nano-grid and the 50 Hz AC grid is realized by a Victron Multiplus 48/3000 hybrid bidirectional inverter [38], which is equipped with two AC outputs. One output is connected to the single-phase AC grid. The second output powers both the e-bike AC charger and the lighting and system monitoring unit, such that the e-bike charging station supports the off-grid operation. Lastly, a Lufft WS503-UMB weather station measures the incoming solar radiation, ambient temperature, and local wind speed for monitoring and research purposes [39]. The system control and monitoring are done using a central Raspberry Pi controller, and a website provides users with feedback and data for scientific research (http://solarpoweredbikes.tudelft.nl).

**Figure 3.** Schematic of the solar e-bike station with 48 V DC interconnection that facilities power exchange between the solar panels, e-bike chargers, and the AC grid.

**Table 1.** Specifications of the solar e-bike charging station.

The power balance equation of the system is:

$$P\_{PV} + P\_{grid} = P\_{batt} + P\_{load} + P\_{loss} \tag{1}$$

where *PPV* is the PV power, *Pgrid* is the power drawn from the grid, *Pbatt* is the charging power of the battery, *Pload* is the power consumption of the station including that of the e-bikes and the baseload, and *Ploss* is the total energy conversion losses.

## *2.2. Charging Demand*

The PV system and storage of the charging station is sized to charge up to five e-bikes and a single e-scooter per day throughout the year, even under low winter insolation. Table 2 shows the specifications of the e-bike, e-scooter, and small car considered for the system sizing [40]. Each e-bike and e-scooter battery has a capacity of 396 Wh and 1920 Wh, respectively. A total of five such e-bikes and one e-scooter, assuming that they arrive with fully drained batteries (the worst case), would translate to a load of 3900 Wh. A baseload of 90 W is consumed by the display, controller, lights, and the weather station, which translates to 2160 Wh. If the charging demand and baseload are combined, the net demand is approximately 6.06 kWh per day. This is the same as the load of one Renault Twizy small electric car. If the charging demand is >6 kWh due to a higher number of e-bikes, then they can be charged by using PV power if the daily PV yield is >6 kWh or using the AC grid power via the DC/AC inverter up to 3 kW\*24 h = 72 kWh per day.

**Table 2.** Specifications of the E-bike and E-scooter.

Three types of e-bike charging methods are developed for the charging station: AC, DC, and wireless charging. The benefit of the AC charging is that it can be universally used for charging all e-bikes, e-scooters, and light EVs by using a charging adapter. On the other hand, for safety reasons, the DC charging is limited to 100 W but has the benefit that the users can simply use a DC cable between the station and the e-bike battery for charging. This DC charging negates the need for an AC power adapter and, hence, provides convenience to the user while plugging in and preventing any possible theft of the adapter. In the case of the wireless charging, the motive is to do away with the user's need for cables altogether, which, therefore, increases the user convenience further [29,30].

## *2.3. Local Storage*

The battery plays a crucial role in providing the o ff-grid capability to the charging station. The station has four lead-acid gel batteries of 220 Ah capacity each. The batteries are series-connected to 48 V and provide a usable capacity of 9.5 kWh, when operated at a maximum depth of discharge of 90%. With ~6 kWh demand per day, it can provide close to 1.5 days of autonomy to the system. Lead-acid gel batteries were preferred due to two reasons. First, they have a much longer lifetime than standard lead-acid batteries, and second, the cost is much lower when compared to lithium-ion batteries.

The preferred charging mode is to charge the batteries directly from the solar panels on DC. In the case of insu fficient solar generation, the battery can be charged (and also discharged) from the AC grid using the DC-AC grid inverter. This bidirectional option can be useful for di fferent grid support services such as peak shaving, reactive power compensation, energy arbitrage, or emergency backup power.

#### **3. PV System Design**

The 2.6 kW PV system is the primary source of power for the e-bike charging station. The PV generation potential is estimated based on solar insolation, wind speed, ambient temperature, panel orientation, and shading due to the surrounding terrain. The meteorological data Cabauw Experimental Site for Atmospheric Research (CESAR) database is used for the PV system modelling, which has a resolution of 1 min [41].

#### *3.1. PV System Modelling*

Using the CESAR data, the incident solar irradiance and the cell temperature of the PV panel are estimated for di fferent azimuth and orientation using Equations (2)–(6) [42–47].

$$G\_{dir(\beta,Am)} = G\_{DNI} \cos(\theta\_i) \tag{2}$$

$$G\_{diff(\beta)} = G\_{DHI} \left( 1 + \cos \beta \right) / 2 \tag{3}$$

$$G\_{\rm gnd} = G\_{\rm GHI} \,\rho \Big(1 - \mu^{\rm sgf} \Big) \tag{4}$$

$$G = G\_{dir(\beta, Am)} + G\_{diff(\beta)} + G\_{alb} \tag{5}$$

$$T\_{\rm cell} = \frac{\Phi G + h\_c T\_{amb} + h\_{r,sky} T\_{sky} + h\_{r,gr} T\_{\rm gr}}{h\_c + h\_{r,sky} + h\_{r,gr}} \tag{6}$$

where *Gdir*, *Gdi f f* , and *Ggnd* are the direct, di ffused, and ground irradiance incident on the module with a tilt β and azimuth *Am*, *GDNI*, *GDNI* and *GGHI* are the direct normal, di ffuse horizontal, and global horizontal irradiance, θ*i* is the angle of incidence of the direct irradiance beam on the panel, Φ = 0.727 is the absorptivity, ρ = 0.2 is the albedo (measured using albedometer), μ*sv f* is the sky view factor, *hc*, is the coe fficient for convective heat transfer *hr*,*sky*, *hr*,*gr* are the coe fficient for radiative heat transfer to the sky, and to the ground, respectively, and *Tamb*, *Tsky*, *Tgr*, and *Tcell* are the ambient, sky, ground, and PV cell temperature, respectively. In this case, the heat transfer coe fficients are estimated using an iterative procedure, where *Tsky* = 0.0522 *T*2/3 *amb*. Lastly, the output power of the PV modules *PPV* can be calculated.

$$P\_{PV} = P\_{STC} \left(\frac{G}{G\_{STC}}\right) \left[1 - \gamma \left(T\_{cell} - 25\right)\right] \tag{7}$$

where *PSTC* = 327 W is the module power at Standard Test Conditions (STC), *GSTC* = 800 W/m2 is the irradiance under STC, η = 20.3% is the e fficiency of the modules, and γ = −0.3%/ ◦C is the module temperature coe fficient for the Sunpower X20-327-BLK modules.

#### *3.2. PV System Orientation*

Based on the meteorological data of 2013, the yield of the PV system is estimated for di fferent fixed orientations. For the whole year, considering a fixed orientation, a tilt of 28◦ and azimuth facing south was found to result in maximum annual yield [6]. Figure 4 shows the average daily yield of the south-facing PV system for each month, considering various fixed tilt angles between 0◦ and 90◦. It can be clearly seen how the tilt angle has a major influence on the monthly yield, especially in the summer months.

Figure 4 also shows the average daily yield if a two-axis tracking system is used. First, it can be seen that the tracking system has a much higher yield as it tracks both the azimuth and tilt when various tilt angles are considered in which the azimuth is fixed. Second, it can be seen that the increase in yield due to tracking is very small for the winter months as most of the irradiance is di ffused irradiance. Due to the higher cost of the tracker and the need for a fixed structure for the bike shelter, tracking systems were not further considered in this study.

**Figure 4.** Average daily yield of the south facing 2.6 kW PV system for each month considering various fixed tilt angles compared to the use of a 2-axis tracker.

#### *3.3. Optimal PV System Design*

In order to get the maximum annual yield, the PV system could have been oriented at a tilt of 28◦ and azimuth facing south. However, the motivation is to ensure that there is sufficient generation in December, which has the lowest solar irradiation in the year. For December, the optimal tilt for maximum monthly yield was 65◦. This is shown in monthly yield estimation for December in Figure 5a for different PV orientations.

**Figure 5.** (**a**) PV system yield (% of maximum) for different orientations considering December month. (**b**) The estimated monthly energy yield of 2.6 kW PV system tilted at 51◦ and facing south.

On the other hand, when the tilt is increased from 28◦ to 65◦, the annual yield is dramatically reduced by up to 20%. Hence, a tradeoff was made to set the tilt angle at 51◦, which results in <5% reduction in annual yield compared to 28◦ and 1% reduction in December yield compared to 65◦.

## *3.4. Shading Analysis*

Since the e-bike station is installed at the ground level, shading from nearby buildings (especially the tall electrical faculty building) has a significant impact on the PV system output, as shown in Figure 6a. To account for the shading due to the adjacent buildings, their 3D models were made using Sketchup. Figure 6b shows the shading caused by the tall Electrical Faculty building in front of the bike station in the afternoon. The plug-in LSS-Chronolux 3D is then used to estimate the sky view factor μ*sv f* = 0.61 and the per minute shading factor *Ssh* as well as the corresponding direct irradiance including the shading, *Gshdir*(β,*Am*):

$$\mathcal{G}^{sh}\_{\text{dir}(\boldsymbol{\beta}\_{\mathcal{A}}\boldsymbol{m})} = \mathcal{S}^{sh}\mathcal{G}\_{\text{dir}(\boldsymbol{\beta}\_{\mathcal{A}}\boldsymbol{m})} \tag{8}$$

#### *3.5. PV System Yield*

Using the 2013 CESAR data and the shading analysis, the PV yield of the system is estimated. The corresponding annual energy yield is 2012 kWh/year, which provides an average daily yield of 5.51 kWh/day, as shown in Figure 5b. Based on the charging demand (Section 2.2), the PV system is sized to a rated power of 2.61 kWp. This means that >90% of the total load demand can be supplied by the PV system on average. At the same time, we have to keep in mind that there is about six times the difference in energy yield between the winter and summer months, as shown in Figure 5b. This results in the load being met on about 180–200 days of the years. That is why a grid connection is required to provide the energy demand in winter, as seen in Equation (1). While the battery cannot provide seasonal storage, it can facilitate off-grid operation and help manage the diurnal solar insolation variation.

**Figure 6.** (**a**) Google Earth image showing the station location and nearby building. (**b**) Calculation of the shading factor due to nearby buildings using Sketchup.

The highly efficient Sunpower X20-327-BLK modules with a relatively high cost per watt peak are chosen since this reduces the total area occupied by the PV system. The tradeoff is that, in return, the lower area significantly reduces the quantity of steel required for the station structure, which has a relatively higher cost for both material and labour.

#### **4. AC E-Bike Charging**

The e-bike charging station provides a single 230 V 50 Hz schuko wall socket for AC charging of e-bikes through the use of a charging adapter. The benefit of the AC charging is that it can be universally used for all light EVs providing up to 16 A charging current, which corresponds to a power of 3.7 kW. This is more than sufficient to charge a small electric car like the Twizy as shown in Figure 2b, which requires a charging current of 10 A. By using the hybrid grid inverter, the AC charging is possible in both grid-connected and an off-grid mode.

#### **5. DC E-Bike Charging**

The DC chargers for the e-bikes should provide galvanically-isolated DC power that is controllable in both output voltage level and maximum charging current, based on the e-bike battery. A dual interleaved quasi-resonant flyback converter with digital current-mode control from Involar is chosen for this task [14,48]. As shown in Figure 7a, the primary side of the flyback is operated directly from the 48 V DC nano-grid. The secondary side of the flyback is connected to the DC cable that the user has to plug into the charging inlet of the e-bike battery. The input current ripple is reduced by half due to the interleaving. The magnetically coupled inductors are wound to have minimum parasitic leakage inductance to reduce the Electromagnetic interference (EMI) and the voltage stress on the MOSFET.

#### *5.1. Current Mode Control*

Depending on the required output power to charge the e-bike battery, the current level in the primary circuit of each flyback is regulated using the current mode control. Each flyback employs two control loops that control the gate of the MOSFET, represented in Figure 7a by the green block, and shown in detail in Figure 8. The inner control is a digitally implemented current mode controller implementing quasi-resonant switching. In this case, the internal current control (curved red line) regulates the amount of energy that is allowed to flow out of the charger and, in that way, sets a limit on the maximum continuous output current, typically 1 A or 2 A. The outer voltage feedback (curved blue line) controls the maximum output voltage of the charger and, accordingly, turns the charger on/off. The maximum output voltage is limited to the nominal voltage expected by e-bike's battery management system, which is typically 24, 36, and 48 volts. A Type-2 control is implemented and is

shown on the left side of the control IC in Figure 8, including the current mode controller and gate driver [49].

**Figure 7.** (**a**) Flyback converter for e-bike battery charging with a digital current mode control and charging current monitoring and logging. (**b**) Simulation results show the primary (red) and secondary (blue) current as well as the charge voltage (Vout = 36 Volt) and charge current (Ich = 2.4 Ampere) for the 48-volt DC grid, and operating in a continuous conduction mode.

**Figure 8.** Current mode control of the flyback converter using an outer voltage loop and an inner current loop.

#### *5.2. Design and Simulation of the Flyback DC Charger*

The flyback converter is designed based on the design procedure for a quasi-resonant operation [10]. Figure 7b shows the simulation of the e-bike charger in Caspoc, where only a single leg flyback is shown for simplicity [50,51]. The multi-level modelling method is employed, which allows the modelling of both the power electronics circuit and the hybrid control consisting of the inner current and outer voltage control loop [52]. The simulation takes into account the delays of the digital feedback compensation as well as the delays caused by switching of the MOSFET. Voltage overshoot caused by the non-coupled parasitic winding inductance is taken into account in the simulation, but not shown here in Figure 7b for clarity.

Figure 7b shows the primary and secondary currents for a duty cycle of 50% in a continuous conduction mode for a switching frequency fs = 100 kHz and turns the ratio of Np:Ns = 48:36. The scope clearly shows the difference in the voltage level on the primary side of 48 V, and secondary side of 36 V, and the charging current of 2.4 A, which is measured on the second scope.

#### *5.3. Safety and Monitoring*

As long as no e-bike is connected, the mechanical on/off switch on the flyback's primary side (Figure 6a) is in the off position. As soon as an e-bike battery is connected to the flyback's secondary side, the voltage is monitored, and the switch is set to the 'on' position. This subsequently powers the flyback converter and its internal control. If either the connection with the e-bike's battery is removed or the charging current drops below the minimum charging current threshold, the outer control turns off the flyback converter by setting the On/Off control to its off position.

#### *5.4. Hardware Realization and Losses*

Figure 9a shows the hardware of the flyback DC charger [48]. On the primary side, IRFS4321PBF MOSFETs with low on-state resistance Rds(on) = 15 mΩ and, on the secondary side, MBR10150 schottky diodes with negligible reverse recovery losses are used. The primary current is sensed using a low ohmic sense resistor placed between the MOSFET source and ground, which is filtered using a low pass first-order filter. In this way, the maximum power is limited by the maximum primary current. The output voltage is measured and, after filtering, sampling is input to the current mode controller. The feedback amplifier with a Type-2 compensation network is replaced by its digital equivalent 2-Pole, 2-Zero compensator [2p2z].

**Figure 9.** (**a**) Interleaved flyback converter for DC e-bike charging. (**b**) The cable used for DC charging. (**c**) Measured charging current of two e-bikes in which each is charged using the flyback based DC e-bike charger.

For the converter losses, the worst-case operating point is at the lowest output voltage of 24 V and maximum power of 100 W, which corresponds to a current of 4.2 A. For the dual interleaved flyback converter, each converter delivers 50 W and 2.1 A current. The MPR10150 diode has a maximum forward voltage *Vf* = 0.6V at 2.1A, conducts only 50% of the time, and, therefore, has *Pd* = 50% (2.1A)(0.6V) = 0.62 W losses. The average input current for each converter and an input voltage of 48V is roughly *Iin* = 1A. Considering a nominal 50% duty cycle, this will give a peak input current Ipk = 4 A and an RMS value of *Iin(rms)* = 1.7 A. This results in a maximum conduction loss of *(Iin(rms))* 2 *Rds(on)* = 44 mW. As a rule of thumb, the switching losses are of the same magnitude of maximum and twice the conduction losses, which gives a total of conduction and switching losses of Psw = 3(44 mW) = 132 mW. Cooling via the PCB is, hence, su fficient in this case.

Figure 9b shows the custom-designed cable that is used to connect the e-bike to the output of the flyback converter in the charging station. The right-side plug is magnetic and, hence, easily attaches to the station, while the left side plug attaches to the e-bike battery. If required, a custom-designed adapter is made for the e-bike side plug based on the manufacturer-specific connector on the user's e-bike. This points to the need for standardization in DC e-bike charging and plugs similar to what was achieved with CCS and CHAdeMO in electric car charging [53].

Figure 9c shows the charging current of two e-bikes in which each was charged using the flyback based DC e-bike charger. For charger 1, the battery gets charged according to the Constant Current, Constant Voltage (CC-CV) principle beginning at a current of 1.3 A, and then slowly reducing to zero. For charger 2, the battery charging begins in the CV region due to the relatively high state of charge (SOC) of the e-bike battery. It can be observed how the e-bike is disconnected at 120 min and then connected again after about an hour.

#### *5.5. Battery Connection Communication*

The internal digital control employs an automated voltage level detection to allow the direct connection to either a 24 V, 36 V, or 48 V battery. An automated output voltage level detection of the battery voltage is achieved through the outer control loop that compares the output voltage to the reference value. The outer control loop subsequently regulates the amplitude of the primary current pulse through the inner loop. The Raspberry Pi is used to control the flyback operation and to monitor the output voltage and charging current. Connected to the Internet, it exchanges and logs information with a server operating in the cloud.

#### **6. Wireless E-Bike Charging**

Wireless charging provides the most convenient and safe experience for the e-bike user. The cyclists do not need to bring along cables and power adapter because the charging process is facilitated through the bike kickstand, as shown in Figure 10. The developed charging system is started once the bike is parked on the appointed parking spot, which is a 30 × 30 cm tile underneath the solar charging station. On the tile, the users have the possibility to park their bike in any position, which makes the wireless charging convenient. On top of that, the wireless charging has intrinsic galvanic insolation, which does not require the users to touch any conductive parts such as cables and connectors that might become dangerous, especially in wet weather conditions.

#### *6.1. Wireless Power Transfer via Resonant Circuit*

Figures 10, 11a and Table 3 shows the schematic, circuit diagram, and specifications of the wireless power transfer system for the e-bike that works based on inductive power transfer through magnetic resonance. In the developed system, the transmitter coil is located under the charging tile, and it is formed by a U-shaped ferromagnetic core with a winding located at the center. On the other hand, the receiver coil consists of the double kickstand of the e-bike. This coil has a similar magnetic circuit as the transmitter coil but, in this case, the ferromagnetic core is closer to a V-shape to resemble the structure of commercial double kickstands.

Once the bike is parked over the tile, the two coils become coupled, which is equivalent to saying that the magnetic circuit becomes closed, and the charging process is ready to start. In a traditional transformer, the coils are strongly coupled because they are wounded around the same ferromagnetic core, and the airgap's order of magnitude is not comparable to the core's dimensions. On the other hand, when the two coils become coupled, the proposed magnetic circuit has two airgaps of about 5 mm, which are less than one order of magnitude smaller than the cross-sectional area of the core. Therefore, in this case, the equivalent transformer has a high leakage inductance and, consequently, is loosely coupled.

**Figure 10.** Block diagram of the wireless power transfer system for the e-bike.

**Figure 11.** (**a**) Simplified circuit of the e-bike wireless charging system. (**b**) Waveforms of the primary and secondary voltages and currents.

**Table 3.** Values of the components of the e-bike wireless charging system.

The capacitors *C*1 and *C*2 compensate the coils' reactive power and form a resonant circuit together with the inductor coils *L*1 and *L*2, which is tuned to the resonant frequency *f*0. The estimation of *C*1 and *C*2 and the Kirchhoff voltage law equations of the circuit schematic can be found in Equations (9) and (10).

$$a\_0 = 2\pi f\_0 \mathbb{C}\_1 = \frac{1}{a\_0^2 L\_1} \mathbb{C}\_2 = \frac{1}{a\_0^2 L\_2} \tag{9}$$

$$\begin{array}{l} \frac{4}{\pi}V\_{in} = \left(j\omega L\_1 + \frac{1}{j\omega \mathcal{C}\_1}\right)I\_1 + j\omega MI\_2\\ 0 = \left(\frac{8}{\pi^2}R\_L + j\omega L\_2 + \frac{1}{j\omega \mathcal{C}\_2}\right)I\_2 + j\omega MI\_1 \end{array} \tag{10}$$

As shown in Figure 10, the wireless charging system draws power directly from the 48 V DC nano-grid. The DC input voltage is then inverted such that a high-frequency voltage supplies the transmitter resonant circuit, which is also called the primary circuit, and the current produces a magnetic field that links to the secondary coil. The high-frequency voltage induced in the secondary circuit is then rectified, and its value is regulated via a DC/DC converter to be supplied correctly to the battery.

#### *6.2. Variable Frequency for Misalignment Tolerance*

Since the cyclist has flexibility in parking the bike, the coupling between the two coils can vary. Moreover, depending on the state of charge of the battery, the loading condition also changes during the charging process. On top of that, the value of the circuit components can change because of an increase in temperature or degradation with time. All these factors make the resonant frequency of the system vary from the designed value. To overcome this problem, the inverter's operating frequency is set by the inner control loop that tracks the natural resonant frequency of the system. For this reason, it is called auto-resonant frequency control. It sets the soft switching of the inverter by predicting the primary current zero-crossing depending on the current slope. This control circuit is simple, analog, fast, and it automatically adapts the operating frequency to the optimum value in a few periods.

#### *6.3. Communication with the Bike and Foreign Object Identification*

The communication between the e-bike and the charging station allows the start-up, shut-down, and foreign object detection. It is realized through backscatter modulation in the power line through amplitude shift keying (ASK) modulation. In this way, the information can be reflected by one side to the other by modulating the voltage of the resonant circuit between a low and high value with a frequency lower than the operating one (about 1 kHz). To send information via ASK from the e-bike to the station, a resistor in series with a switch connected across the output DC voltage is used. When the switch is in an open or close position, ASK assumes a high or low value (due to a voltage drop across the resistor), respectively. These binary values can be organized in sets of bits that form the messages of the communication system. Once a message is received, it is demodulated and interpreted for execution.

The wireless charging system has to be able to detect and stop the charging process if a foreign object is placed on the surface of the transmitter coil. This is because the magnetic field can potentially heat the object through eddy currents, and could lead to fire or injury. This is because either the charging process is not started or the ongoing charging process is stopped if a foreign object is detected. The first scenario is avoided because the charging begins only if proper communication messages are sent by the e-bike. On the other hand, e fficiency measurements of the power transfer can indicate if a foreign object is receiving part of the transferred power, which results in a lower e fficiency level than expected.


**Appendix A**

**Table A1.** Nutritional compositions of the experimental diets.

**Table A2.** Effects of different concentrations of purple rice husk extract (PRHE) on metabolic indexes and renal function (a preliminary study).

Values are mean ± SD (*n* = 4). NDV: vehicle-treated normal group; DMV: vehicle-treated diabetic group; DME (150, 300, 600): PRHE-treated diabetic group at 150, 300, 600 mg/kg, respectively; BW: body weight; HOMA-IR: homeostasis model assessment of insulin resistance; KW: kidney weight. \* *p* < 0.05 vs. NDV, † *p* < 0.05 vs. DMV, # *p* < 0.05 vs. DME 300.

#### **References**


**2. Overview of Distribution-Level VGI Control**

A radial distribution system is the simplest and the most commonly seen power distribution system. When massive electric vehicle charging activities present in a radial distribution system, the system is at the risk of aggravated power loss and excessive voltage drop. A distributed PEV charging management scheme is designed to provide emergent voltage recovery in a distribution grid and PEV charging power allocation at local microgrids. Figure 1 shows the control and communication among the components of a distribution-level VGI system. Over the feeder branch, a grid-level agent takes charge of bus voltage monitoring and voltage regulation. This consists of modules for bus voltage monitoring, voltage–load variation relation analysis, ADMM optimization, and the distributed negotiation service. At each bus, the buildings and PEV charging stations are grouped to form a microgrid-level VGI system. A microgrid agent is composed of the PEV charging control module that is responsible for dispatching the limited charging power to individual PEVs and the ADMM optimization module, which coordinates with other agents during the process of voltage recovery negotiation.

**Figure 1.** The physical components and control architecture of a distribution-level VGI system.

When the grid-level agent senses a voltage violation at a bus, it sends a notification to all the VGI microgrid agents. Each microgrid individually finds and decreases the microgrid PEV charging power to a critical charging power point. This critical charging power point is the lowest microgrid PEV charging power capacity that meets the PEV state of charge (SOC) requirements. If the voltage violation remains, a voltage–load relation analysis is conducted by the grid-level agent. The coefficients of the voltage–load relation, which reflects the impact of the load change of individual microgrids on voltage variation at current violation bus, are broadcasted to all the agents in the network. A negotiation is initiated among all the involved microgrid agents and the grid-level agent. The negotiation uses the ADMM-based method to find out the balance between the load curtailment amount of each microgrid

and the voltage recovery requirement. Figure 2 shows the PEV charging power capacity determination process before the microgrid PEV charging control can be applied.

**Figure 2.** The determination of PEV charging power capacity in microgrids.


6.1.2. Performance Metrics

In terms of metric evaluation performance, several performance metrics were selected to measure the robustness of the proposed method compared to the other existing work. We divided performance metrics into a few parts. Error evaluations consist of a standard error metric, such as a root mean square error (*RMSE*) and a mean absolute error (*MAE*). The formulation for both errors are as follows:

$$RMSE = \sum\_{i=1}^{n} \frac{1}{n} \left( Q\_i^{test} - Q\_i^B \right)^2 \tag{16}$$

$$MAE = \sum\_{i=1}^{n} \frac{1}{n} \left| \mathbf{Q}\_i^{test} - \mathbf{Q}\_i^B \right| \tag{17}$$

where *Qtest i* is the state of the data *Qtest i* ∈ {−1, 1}. In detail, the best logic mining model will produce the *QB i* with the lowest error evaluation. Next, standard classification metrics, such as accuracy, *F-score*, precision, and sensitivity will be utilized in the experiment. According to [35], the sensitivity metric *Se* analyses how well a case correctly produces a positive result for an instance that has a specific condition. Note that, *TP* (true positive) is the number of positive instances that correctly classified, *FN* (false negative) is the number of positive instances that incorrectly classified, *TN* (true negative) is the number of negative instances that correctly classified, and *FP* (false positive) is the number of incorrectly classified positive instances.

$$Se = \frac{TP}{TP + FN} \tag{18}$$

Meanwhile, precision is utilized to measure the algorithm's predictive ability. Precision refers to how precise the prediction is from those positively predicted with how many of them are actually positive. The calculation for precision (*Pr*) is defined as follows:

$$Pr = \frac{TP}{TP + FP} \tag{19}$$

Accuracy (*Acc*) is generally the common metric for determining the performance of the classification. This metric measures the percentage of instances categorized correctly:

$$Acc = \frac{TP + TN}{TP + TN + FP + FN} \tag{20}$$

As stated by [36], *F-score* is a significant necessity that reflects the highest probability of correct result, explicitly representing the ability of the algorithm. Additionally, F1-score is described as the harmonic mean of precision and sensitivity. Next, the Matthews correlation coefficient (*MCC*) will be used to examine the performance of the logic mining based on the eight major derived ratios from the combination of all components of a confusion matrix. *MCC* is regarded as a good metric that represents the global model quality and can be used for classes of a different size [37].

$$F\text{ Score} = \frac{2TP}{2TP + FP + FN} \tag{21}$$

$$\text{MCC} = \frac{TP \ TN - FP \ FN}{\sqrt{(TP + FP)(TP + FN)(TN + FP)(TN + FN)}} \tag{22}$$

It is worth mentioning that this is our first encounter to approach logic mining with various performance metrics. In [20,22], the only metric used is only accuracy and testing error.


**Reproducibility**

One of the key factors facilitating the development of new materials in engineering disciplines is the adoption of a comprehensive and reliable set of standards. Such levels of standardisation are difficult to achieve in the context of biological systems, which often have a high degree of uncertainty. This condition does not refer exclusively to the complexity of the biological systems themselves, but to the availability of infrastructures that have the capacity to support the development of standardised practices. Specifically, one of the main limitations to overcome is the conversion of the laboratory scale protocols into much larger and industrial-size equivalents.

### **Automation**

The use of machines and new manufacturing technologies in highly specialised and controlled environments is an important challenge shared by industries ranging from biomedicine, chemistry, and biotechnology to architecture. Weariful and repetitive tasks that are prone to error when done by humans are already being accomplished in many laboratories and industries with robots. This includes tasks such as liquid handling, as well as additive and subtractive manufacturing. The use of machines in environments such as the ones needed to produce mycelium-based materials creates a condition that intrinsically minimises human intervention, ensuring a safe environment during the fabrication process.

### *3.6. Agile In Situ Setup and Applications*

In large part, mass-produced mycelium-based materials that have seen commercial success consist of MBC grown in moulds. This fabrication method relies on a closed, sterile environment. Growing mycelium in situ without a controlled environment has only seen success on a small-scale, while 3D printing has similarly succeeded on a small scale and within a controlled environment. Scaling up in situ applications requires a better understanding of preventing contamination, as well as the development of techniques that do not rely on such a restrictive, closed-off space. Whereas prefabricated MBC can rely on moulds to ensure a closed environmental system, when scaling up there is a need for this controlled environment to be larger than just the mould of one module.

Implementation of new fabrication techniques with mycelium-based materials in a controlled, sterile environment at an architectural scale presents a number of challenges. For example, the use of a robotic arm for mycelium prototyping requires a cleanroom with accurate humidity and temperature control. This constraint is an opportunity to create a walk-in prototyping space able to facilitate the production of large-format prefabricated and highly customised parts. These environments can potentially be located adjacent to the construction site, ultimately reducing costs of transport.

Within this framework, it may be even possible to consider these controlled environments as new construction sites, wherein prefabricated parts and substrates are simply installed and subsequently bound together using the binding capacity of the mycelial network. By approaching the controlled environment as the construction site, it is possible to develop an in situ bio-assembly process in which building parts are naturally bound and mechanically enhanced.

### *3.7. Cross-Disciplinary Research*

The relevance of mycelium-based materials, as demonstrated in this paper, is not limited to their original fields of microbiology and mycology. Thanks to the work of researchers across many disciplines, their relevance has expanded into architecture. These fields are practically unrelated by tradition and in practice. However, the research and development to explore bio-based materials in general have drawn them closer together. While this increases the scope of research and development being done with these materials, it also means that this information does not easily cross over into the other fields. Furthermore, whenever that information does make the jump between disciplines, it can be difficult for an architect, for example, to discern the findings of a microbiologist. Therefore, the cohesive understanding of latest findings and material developments presents its own challenge as many factors prevent the knowledge from being distributed among experts from different industries.

As research on mycelium-based materials continues to exponentially increase year after year, the accessibility of the research also increases. This indicates that the visibility of research increases, and thus the dissemination of information across fields of research will naturally increase. Active efforts to regularly collaborate across different industries can best promote the sharing of findings and development of new technologies. The conscientious integration of the advances from different fields can also further unify the industries and spur new research. In this way, several other previously mentioned challenges in scaling up applications of mycelium-based materials can be addressed, such as the generalisation of how substrates, fungal strains, and environmental conditions affect the growth of mycelium. A further integrated approach can also begin to establish new, open-source practices for goals such as mass-production to expedite the scaling up of mycelium-based materials.


**About the Special Issue Editors**

**Robert K. Logan**, Prof., (Ph.D. MIT 1965) is an Emeritus Professor of physics, a fellow of St. Michael's College, and a member of the School of the Environment at the University of Toronto. He is also the Chief Scientist at OCAD (Ontario College of Art and Design) University in the Strategic Innovation Lab. He has a variety of experiences as an academic and has engaged in research in a variety of fields, including physics, media ecology, complexity theory, information theory, systems biology, environmental science, linguistics, and industrial design. He has published with and collaborated with Marshall McLuhan and continues his McLuhan studies research. He is also an author or editor of 19 books, 23 book chapters, and over 150 articles in refereed journals. He once taught The Poetry of Physics and the Physics of Poetry course at the U. of Toronto and has published a book with the same title as that of the course. He was the recipient of the Walter J. Ong Award for Career Achievement in Scholarship by the Media Ecology Association.

**Adriana Braga** is an Associate Professor of the Graduate Program of Social Communication at the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), researcher of the National Council for Scientific and Technological Development (CNPq/Brazil), coordinator of the Research Group on Digital Interactions (GRID) and is a Visiting Professor at the University of Macau, China. With a Ph.D. in Communication Sciences and BA in Psychology, she is also the author of the books *Introdu¸c˜ao a Ecologia das M´ ` ıdias* (*Introduction to Media Ecology* with Lance Strate and Paul Levinson, 2019); *Personas Materno-Eletrˆonicas* (*Maternal-Electronic Personae*, 2008); *CMC, Identidades e G´enero* (*CMC, Identities and Gender*, in Portugal, 2005); and *Corpo-Ver˜ao* (*Summer-Body*, 2016). Her Ph.D. dissertation won the CAPES Thesis Award (MEC/Brazil) and Harold Innis Award (Media Ecology Association/USA). She serves as Vice President of the Media Ecology Association. She currently coordinates the Digital Media Lab (LabMiD), which develops projects in digital communication, literature, Brazilian culture, mobile telephony, digital literacy, gender, sociolinguistics, and technology.


*4.2.2 Process-related risks*

*<IP_ADDRESS> Informed consent is invalid although it is stored as valid in the system*

#### **Possible reasons**

• Invalidity of the IC cannot be detected during scanning and monitoring and therefore is stored as valid in the system

#### **Possible consequences**

#### **Possible coping strategy**

#### *<IP_ADDRESS> Collecting wrong clinical data through biobank delegates*


*4.4. Economic Comparison*

In this section, the traditional two-column process with an excess of butyric acid and the proposed two applications of the SRDC process are compared from the perspective of TAC, the calculation of which is shown in Equation (13) [12], consisting of capital cost and energy cost. The capital cost includes the cost of the column, tray, and heat exchanger, and the energy cost includes the cost of steam and cooling water. As shown in Table 4, the TAC of the three processes are calculated and compared.

$$TAC = \text{capital cost} / \text{payback period} + \text{energy cost} \tag{13}$$

As seen in Table 4, both SRDC processes are more economical than the traditional two-column process. In addition, the excess of butyric acid is more economical than the excess of acetic anhydride for the SRDC process; this condition is the result of the removal of the second distillation column,

**Table 4.** Economic comparison.


**5. References**

**Part 3**

**Thermo-Fluid Systems**

**Part 3**

**Thermo-Fluid Systems**

352 Mechanical Engineering

Bicchi, A., Tonietti A. (2004) Fast and "soft-arm" tactics. *IEEE Robotics and Automation*

Bögelsack, G. (1995). Nachgiebige Mechanismen in minaturisierten Bewegungssystemen,

Christen, G., Pfefferkorn, H. (1998). Nachgiebige Mechanismen, *VDI Berichte Nr. 1423*, 1998 Griebel, S., Fiedler, P., Streng, A., Haueisen, J., Zentner L. (2010). Medical sensor placement

Ham, R., Sugar, T., Vanderborght, B., Hollander, K., Lefeber, D. (2009) Compliant actuator

Howell, L. L. (2001). *Compliant Mechanisms*, John Wiley & Sons, ISBN<PHONE_NUMBER>786, New

Linß, S., Zentner, L., Schilling, C., Voges, D., Griebel, S. (2008). Biological inspired

Risto, U., Zentner, L., Uhlig, R. (2008). Elastic structures with snap-through characteristic for

Shuib, S., Ridzwan M. I. Z., Kadarman, H. (2007). Methodology of Compliant Mechanisms

Zentner, L. (2003). Untersuchung und Entwicklung nachgiebiger Strukturen basierend auf

Zentner, L., Böhm, V., Minchenya, V. (2009). On the new reversal effect in monolithic

Zentner, L., Böhm, V. (2009). On the classification of compliant mechanisms. *Proceedings of*

Zinn, M. Khatib, O., Roth, B., Salisbury, J. K. (2004). A new actuation approach for human

*Theory*, Vol.44, No.5, (May 2009), pp. 1009–1018, ISSN 0094-114X

*Applied Sciences*, Vol.4, No.3, (March 2007), pp. 160-167, ISSN 1546-9239 Wang, W., Loh, R. N. K., Gu, E. Y. (1998) Passive compliance versus active compliance in

*Proceedings of the 9th World Congress on Theory of Mach. and Mech.*, Milano, August-

with a screw motion. *Proceedings of Actuator 10 / International Conference on New*

designs. IEEE Robotics and Automation Magazine, Vol.16, No.3, (September 2009),

development of suction cups. *Proceedings of 53. Internationales Wissenschaftliches Kolloquium, IWK. Technische Universität Ilmenau*, ISBN 978-3-938843-37-6, Ilmenau,

closing devices. *Proceedings of 53. Internationales Wissenschaftliches Kolloquium, IWK. Technische Universität Ilmenau*, ISBN 978-3-938843-37-6, Ilmenau, September 2008 Risto, U., Uhlig, R., Zentner, L. (2010). Thermal controlled expansion actuator for valve

applications. *Proceedings of Actuator 10 / International Conference on New Actuators*,

and its Current Developments in Applications: A Review. *American Journal of*

robot based automated assembly systems. *Industrial Robot*, Vol.25, No.1, (1998), pp.

innendruckbelasteten Röhren mit stoffschlüssigen Gelenken, *Ilmenau ISLE Verlag*,

compliant bending mechanisms with fluid driven actuators. *Mechanism and Machine*

*EUCOMES 08 The Second European Conference on Mechanism Science*, ISBN 978-1-

friendly robot design. *The International Journal of Robotics Research*, Vol. 23, No.4-5,

*Magazine*, Vol. 11, No. 2, (June 2004), pp. 22-33, ISSN 1070-9932

*Actuators*, ISBN 978-3-933339-12-6, Bremen, June 2010

http://www.iftomm.3me.tudelft.nl/2057/frames.html

ISBN 978-3-933339-12-6, Bremen, June 2010

September 1995

September 2008

48-57, ISSN: 0143-991X

2003, ISBN 3-932633-77-6

4020-8914-5, Cassino, September 2009

(2004), pp. 379 – 398, ISSN 0278-3649

York

pp. 81-94, ISSN 1070-9932

IFToMM Terminology, Version 2.3 – April. 2010,

**16**

 *Taiwan*

**Waste Heat Recycling for Fuel Reforming**

At the current rate of consumption, it is estimated that the crude oil reserves of the entire world will be depleted in less than 40 years. Due to an unstable international situation, the price of crude oil keeps rising, compelling the price of other fossil energies to soar. Because of its high dependency on imported energy, Taiwan is deeply affected by the crude oil price. Additionally, because of its low cost and high carbon number, fossil fuel has been the major fuel used in Taiwan. The enormous number of vehicles and motorcycles in Taiwan emit excess amounts of carbon dioxide, sulfur, and nitrogen; consequently, the level of greenhouse gas emissions in Taiwan far exceeds that of other countries. Because coal, natural gas, and petroleum will still be the major sources of energy in the short to medium term future; and since increasing energy efficiency is presently a globally acknowledged strategy; the use of new energy technologies for the transformation of traditional fuels into clean fuels - such as clean coal, fuel cells, and fuel reforming - is being promoted all over the world. Combining all the factors described, it has become necessary to quickly develop new energy and combustion techniques with existing equipment and resources to reduce air

Plasma-assisted reforming of hydrogen production is a promising energy utilization technology, and an increasing number of research units are conducting related experiments around the world. Plasma-assisted production of hydrogen is different from traditional hydrogen production approaches, and the equipment involved in the plasma-assisted production of hydrogen is small, easy to start, and economical; it can also increase overall thermal efficiency of internal combustion engine by combining with the hydrogen produced

The method of hydrogen production by plasma or catalytic converters with various hydrocarbon fuels has been studied by various researchers internationally. An arc generated plasma was used to facilitate the reforming reaction by heating. The heated mixture could be distributed to all of the passages in the reaction chamber by arc rotation driven by magnetic field. Water vapor could be directed into the system to lower the temperature of the electrodes via heat recycling to prevent the reaction region from over-heating, therefore extending the life of the system (Bromberg et al., 1997). The optimum settings and the cost of

**1. Introduction**

pollution produced by combustion.

from the fuel reforming system.

Rong-Fang Horng1 and Ming-Pin Lai2 *1Department of Mechanical Engineering, Kun Shan University, Tainan City,*

*2Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan City,*


**2. Preparation of carbonaceous mesophase**

The formation of mesophase pitch is a phase inversion process (transformed from isotropic to anisotropic), which is a result in which the pitch precursor undergoes thermal decomposition and thermal polycondensation to a certain extent. Nowadays, either thermal polycondensation of commercial coal-tar pitch and petroleum pitch (or even their certain soluble fractions) or catalytic polymerization of some aromatic substances is commonly used to prepare the carbonaceous mesophase [5]. It needs to be emphasized that carbonaceous precursors for the preparation of high-performance mesophase pitch are very crucial. Although commercial coal-tar pitch or petroleum pitch or heavy oil is very cheap and easy to obtain, these feedstocks are normally a complex mixture including with some heteroatoms and inorganic ash (~0.2 wt.%), which makes a spinnable mesophase pitch difficult to prepare [6–8]. So far, it is still very hard to massively produce cheap mesophase pitch with high quality, especially for continuously melt spinning high-performance carbon fibers. In recent 30 years, small model aromatic compounds (such as naphthalene, methylnaphthalene, anthracene, etc.) have been widely used to synthesize spinnable mesophase pitch by catalyzing with superacid, HF-BF3. The obtained naphthalene-derived mesophase pitch possesses characteristics of high purity, controllable molecular structure, and ideal physical property [3, 9–11]. However, the severe corrosion problem and potential operating risk of using HF-BF3 as a catalyst will unfortunately limit its widespread use (and such a mesophase pitch product named "AR" as shown in **Figure 2(a)** is now no longer available from, e.g., Mitsubishi Gas Chemical Company).

**103**

**Figure 2.**

**Figure 3.**

*naphthalene.*

reaction conditions adopted [2, 15, 16].

*Preparation, Characterization, and Applications of Carbonaceous Mesophase: A Review*

In the meantime, a mild catalyst AlCl3 has been selectively used to prepare the mesophase pitch from the simple molecules and achieve the anticipated catalytic polymerization effect in spite of a trace of residual catalyst (e.g., 300–1000 ppm) inevitably intermingled in the mesophase pitch [3, 12–14]. **Figure 3** shows the flow diagram of catalytic thermal polymerization of naphthalene molecule to prepare carbonaceous mesophase pitch as shown in **Figure 2(b)** by a two-step reaction process at a liquid-phase carbonization temperature of 350–450°C for a certain period of time. It could be concluded that some carbonaceous precursors (e.g., naphthalene) have undergone four stages of liquid-crystalline sphere development and transformation and finally formed a bulk liquid-crystalline mesophase from an isotropic matrix as illustrated in **Figure 4** under a suitable reaction condition (i.e., reaction temperature and time) [2, 15, 16]. The general four-stage conversion of liquid crystals during the whole process is diagramed as follows: (I) generation of optically anisotropic spheres in isotropic matrix, (II) growth of anisotropic spheres in isotropic matrix, (III) coalescence of anisotropic spheres in isotropic matrix, and (IV) deformation and disintegration of anisotropic coalesced spheres to form bulk liquid-crystalline mesophase. However, it has been demonstrated that the formation, development, and transformation of liquid-crystalline anisotropic spheres (i.e., nucleation, growth, coalescence and deformation and orientation) in an isotropic pitch matrix are unconcerted and inhomogeneous during the process of liquid-phase carbonization as shown in **Figure 5** [16]. Furthermore, it is not easy to obtain a 100 vol.% anisotropic mesophase pitch (i.e., bulk mesophase) both with a fine flow optical texture and an acceptable softening point less than 300°C for subsequent fiber spinning. This mainly depends on the carbonaceous precursors (e.g., molecular unit size, the flatness of molecules and the chemical reactivity, etc.) and the suitable thermal

*Flow diagram of catalytic thermal polymerization of naphthalene molecule to form carbonaceous mesophase*

*pitch ((LT) a low temperature of ~200°C and (MT) a mid temperature of ~430°C).*

*Optical photographs of (a) pellet and (b) block-shaped carbonaceous mesophase pitch derived from*

*DOI: http://dx.doi.org/10.5772/intechopen.88860*

*Preparation, Characterization, and Applications of Carbonaceous Mesophase: A Review DOI: http://dx.doi.org/10.5772/intechopen.88860*

#### **Figure 2.**

*Liquid Crystals and Display Technology*

*industrial and engineering carbon products.*

**Figure 1.**

mesophase pitch, it has become a high-quality precursor material for fabricating high-performance and multifunctional carbon materials as shown in **Figure 1**, such as mesophase pitch-based coke, needle coke, high-power graphite electrodes, mesocarbon microbeads (MCMBs), mesophase pitch-based carbon foam, mesophase pitch-based carbon fibers with high modulus and thermal conductivity, good binder and impregnating agent for high-thermal conductivity carbon-based composites, etc. Therefore, there is no doubt that mesophase pitch occupies a pivotal and irreplaceable position in various fields, such as defense, military, aerospace, cutting-edge

*Main promising applications of carbonaceous mesophase as an excellent precursor for making a wide variety of*

The formation of mesophase pitch is a phase inversion process (transformed from isotropic to anisotropic), which is a result in which the pitch precursor undergoes thermal decomposition and thermal polycondensation to a certain extent. Nowadays, either thermal polycondensation of commercial coal-tar pitch and petroleum pitch (or even their certain soluble fractions) or catalytic polymerization of some aromatic substances is commonly used to prepare the carbonaceous mesophase [5]. It needs to be emphasized that carbonaceous precursors for the preparation of high-performance mesophase pitch are very crucial. Although commercial coal-tar pitch or petroleum pitch or heavy oil is very cheap and easy to obtain, these feedstocks are normally a complex mixture including with some heteroatoms and inorganic ash (~0.2 wt.%), which makes a spinnable mesophase pitch difficult to prepare [6–8]. So far, it is still very hard to massively produce cheap mesophase pitch with high quality, especially for continuously melt spinning high-performance carbon fibers. In recent 30 years, small model aromatic compounds (such as naphthalene, methylnaphthalene, anthracene, etc.) have been widely used to synthesize spinnable mesophase pitch by catalyzing with superacid, HF-BF3. The obtained naphthalene-derived mesophase pitch possesses characteristics of high purity, controllable molecular structure, and ideal physical property [3, 9–11]. However, the severe corrosion problem and potential operating risk of using HF-BF3 as a catalyst will unfortunately limit its widespread use (and such a mesophase pitch product named "AR" as shown in **Figure 2(a)** is now no longer available from, e.g.,

technology, high-end industrial manufacturing, etc. [2, 3, 5].

**2. Preparation of carbonaceous mesophase**

**102**

Mitsubishi Gas Chemical Company).

*Optical photographs of (a) pellet and (b) block-shaped carbonaceous mesophase pitch derived from naphthalene.*

#### **Figure 3.**

*Flow diagram of catalytic thermal polymerization of naphthalene molecule to form carbonaceous mesophase pitch ((LT) a low temperature of ~200°C and (MT) a mid temperature of ~430°C).*

In the meantime, a mild catalyst AlCl3 has been selectively used to prepare the mesophase pitch from the simple molecules and achieve the anticipated catalytic polymerization effect in spite of a trace of residual catalyst (e.g., 300–1000 ppm) inevitably intermingled in the mesophase pitch [3, 12–14]. **Figure 3** shows the flow diagram of catalytic thermal polymerization of naphthalene molecule to prepare carbonaceous mesophase pitch as shown in **Figure 2(b)** by a two-step reaction process at a liquid-phase carbonization temperature of 350–450°C for a certain period of time. It could be concluded that some carbonaceous precursors (e.g., naphthalene) have undergone four stages of liquid-crystalline sphere development and transformation and finally formed a bulk liquid-crystalline mesophase from an isotropic matrix as illustrated in **Figure 4** under a suitable reaction condition (i.e., reaction temperature and time) [2, 15, 16]. The general four-stage conversion of liquid crystals during the whole process is diagramed as follows: (I) generation of optically anisotropic spheres in isotropic matrix, (II) growth of anisotropic spheres in isotropic matrix, (III) coalescence of anisotropic spheres in isotropic matrix, and (IV) deformation and disintegration of anisotropic coalesced spheres to form bulk liquid-crystalline mesophase.

However, it has been demonstrated that the formation, development, and transformation of liquid-crystalline anisotropic spheres (i.e., nucleation, growth, coalescence and deformation and orientation) in an isotropic pitch matrix are unconcerted and inhomogeneous during the process of liquid-phase carbonization as shown in **Figure 5** [16]. Furthermore, it is not easy to obtain a 100 vol.% anisotropic mesophase pitch (i.e., bulk mesophase) both with a fine flow optical texture and an acceptable softening point less than 300°C for subsequent fiber spinning. This mainly depends on the carbonaceous precursors (e.g., molecular unit size, the flatness of molecules and the chemical reactivity, etc.) and the suitable thermal reaction conditions adopted [2, 15, 16].

#### **Figure 4.**

*Schematic illustration of the formation and development process of bulk liquid-crystalline mesophase under a suitable reaction condition (scale bar in PLM micrographs is 100 μm).*

#### **Figure 5.**

*(a) Polarized light microscope (PLM) micrograph of the naphthalene-based synthetic pitch and (b) SEM image of broken surface of the pitch-derived coke showing an unsynchronized and inhomogeneous conversion of liquid-crystalline anisotropic spheres.*


**9. Summary of my Learnings**

Although my journey in understanding CaC as an anti-racist pedagogy has just begun, what I have learned, so far, is that CaC can be understood as anti-racist in its facilitation of the empowerment of Campesinos/as to work toward their own version of sustainability. To be anti-racist, education, including education for sustainability, must value diverse cultural knowledge and be culturally rooted to be relevant for learners [10]. By centering Campesinos/as as protagonists, CaC values diverse cultural knowledge and ways of teaching and learning that originate in each context where it is practiced. By working through peer relationships CaC sets up fundamentally different ways of teaching and learning from that of traditional agricultural extension. Although there may always be a power imbalance between those who have the knowledge and those who want to learn, the balance of power between those who consider each other to be peers is importantly different. Peers assume competence in each other; thus, the relations of power are more balanced. Each has something to learn from the other. CaC operates in ways that assume and build the power of those who have been marginalized by dominant systems.

CaC makes empowerment real, not by giving or lending power, but by assuming it is already there. This power is predicated on a fundamental belief by the facilitating organization in the capacity of Campesinos/as as holders of knowledge that will lead them toward self-sufficiency. This knowledge is not based on an abstract concept of 'development' created far away, but on knowledge and experience of the local context that is needed for building sustainable livelihoods in this context. This valuing of Campesino/a knowledge and expertise is unique in a society that continues to treat Campesinos/as as "less than". To move toward anti-racist approaches to education for sustainability we must interrogate the history of 'development' and how it affects our conceptions of who has the expertise to decide what sustainable development looks like. By teaching agroecology, CaC contributes to the revitalization and learning of traditional Indigenous agricultural practices and knowledge that may have been erased through colonial processes.

This paper illuminates researcher reflexivity as crucial in the process of decolonizing research and transitioning to anti-racist approaches to education and sustainable development. I shared the story of my learning journey to demonstrate how my white privilege and unexamined biases limited my understanding and representation of CaC and the perspectives of my research participants. My experiences unsettled the hegemony of 'Western' knowledge in my own thinking and led to moments of epistemic friction that gave me pause. In these pauses, I opened my mind to new ways of understanding CaC pedagogy. This matters because I am now able to recognize and interrogate systems that continue to marginalize Campesinos/as and can work to bring their knowledge and perspectives to discussions of education for sustainable development where 'Western' discourses are still accepted as the norm.

Through reflecting on my research journey, I have learned several ways that researchers can unsettle the hegemony of 'Western' knowledge and revalue marginalized ways of knowing and being. First, learn about the colonial history and social relations of power (race, class, gender), that affect each context. My learning journey reinforced that racism operates differently in each political, cultural and historical context and is greatly influenced by colonial histories. Learning this critical context is especially important in cross-cultural situations to understanding the perspectives of participants and communicating their interests and aims. CEE as a methodology was helpful in guiding me toward understanding the critical context including how racialization, gender, class, Indigenous identity, and narratives of 'underdevelopment' influence the daily lives of Campesinos/as. These understandings can be used to transform public perception about Campesinos/as and "interrupt and reframe the victim-blaming mantras" [47] (p. 169).

Second, consider the history of research itself and how it may perpetuate colonial relations of power. Learning about and reckoning with the histories of anthropology, forced me to consider every step and decision in my research process in a new way. To decolonize our research methodologies, we must interrogate the history and purposes of research and find ways to involve subjects of research in all stages of design and elaboration of study so our research can be in a reciprocal relationship with participants. Part of this learning involves navigating the privilege of doing research and offering our labor as researchers in reciprocity for the efforts of our participants. Changing the research relationship to one of collaboration revalues the knowledge and expertise of participants and moves research away from its extractive history.

Thirdly, expand and shift the scholars we consider in our analysis. Engaging with scholarly work that originates outside dominant 'Western' ways of knowing is necessary to understanding diverse manifestations of sustainability and education for sustainability. The exercise of trying to make CaC fit into 'Western' theories of teaching and learning was useful in the sense that it allowed me to see where CaC was different. Encountering decolonization theory forced me to consider that my attempt to explain CaC through 'Western' social learning theories was perpetuating the imperialism of Western/Euro-American knowledge. I had fallen into the unconscious pattern of assuming that 'Western' knowledge is universal, and in doing so neglected to consider ways of teaching and learning as they are understood in other cultures.

As I shared at the beginning, this is only a part of my story, and my learning journey continues. As a white settler scholar, I have much to learn and reckon with, but through the sharing of my reflections, mistakes, and limitations, I invite others to also engage in reflexive practice. As Fortier writes, "the responsibility of non-Indigenous peoples to engage in self-reflexive and critical actions and research around their own relationships to colonialism

and the process of decolonization is important in the development of self-determining relationships of solidarity" [19] (p. 22).

**Funding:** This research was supported by the Ontario Graduate Scholarship Program, and Queen's University.

**Institutional Review Board Statement:** The doctoral study, which this paper is based upon was conducted in accordance with the Ethics Review Board of Queen's University.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Acknowledgments:** I acknowledge that this research would not have been possible without the staff of Fondo para la Paz and the Campesinas and Campesinos of Calakmul who welcomed me and shared their perspectives. I am grateful for the careful attention and thoughtful suggestions of my dissertation committee and the three peer reviewers of this article. I also thank Charles Levkoe, and Ranjan Datta for their support in developing this article.

**Conflicts of Interest:** The authors declare no conflict of interest.


*2.5.2 Biochar*

Biochar is the charred solid material obtained from the carbonization of biomass. The most common method of production of biomass is by pyrolysis which is the thermal decomposition of biomass in absence or limited oxygen. Biochars are less carbonized than AC so more carbon, hydrogen and oxygen remain in their structure. Biochar has shown remarkable potential for remediation of heavy metals from wastewater than other conventional and low-cost adsorbents. They have a mesoporous structure leading to a high surface area and presence of different functional groups and a low ash content makes them excellent and effective adsorbents. Feedstock such as rice husk [180–185], corn husk [180], tea waste [181, 184, 186, 187] and digested sludge have [185, 188] been employed for removal of heavy metals from aqueous solutions as well as wastewater.


**12. Discussions**

Respondents emphasized the critical need for inclusivity in STEM education and the importance of dismantling barriers to ensure equal opportunities for all genders. This aligns with BrckaLorenz et al. [34], who stress the importance of recognizing diverse voices and fostering equal opportunities in STEM. Moreover, the commitment of one respondent to challenging societal expectations regarding gender and career choices echoes the findings of Andrews and Boklage [35], who emphasize the role of societal expectations in influencing STEM career paths. The concern over the lack of representation of women in STEM leadership positions is supported by Killpack and Melón [36] and Leslie et al. [37], who highlight the underrepresentation of women in STEM leadership roles [38]. However, the emphasis on visibility and role modeling from a parental perspective adds a nuanced dimension, suggesting a grassroots approach to fostering empowerment in STEM.

Respondents advocated for progressive teaching strategies that cater to diverse learning styles and promote inclusivity in STEM education. This aligns with Lincoln et al. [39], and Moriarty [40], who emphasize the importance of inclusive spaces in fostering equitable participation. Additionally, the endorsement of active learning methodologies resonates with Freeman et al. [41], who highlight the benefits of active learning in promoting critical thinking and deeper understanding. While the emphasis on inclusivity and project-based learning is consistent with previous research, the specific mention of digital tools and platforms to enhance engagement reflects a contemporary approach to pedagogy, suggesting an adaptation to modern learning environments.

Respondents stressed the importance of policy interventions to promote gender equity in STEM, including targeted recruitment and retention policies and mentorship programs. This aligns with Laursen et al. [16] and Brush et al. [42], who highlight the effectiveness of targeted policies in increasing women's participation in STEM

fields. However, while previous research emphasizes the importance of policies, this study adds a focus on workplace policies promoting flexibility and work-life balance, acknowledging the intersectionality of gender and other factors in STEM careers.

Respondents discussed local challenges such as socio-cultural norms and resource limitations, highlighting the importance of community engagement and policy support. This aligns with Tanwir & Khemka [43] and [44], who stress the significance of identifying barriers specific to the community while leveraging local strengths. The emphasis on collaborative efforts and grassroots initiatives adds depth to the discussion, suggesting a bottom-up approach to addressing gender equity in STEM education. This differs from previous research by highlighting the importance of community-led initiatives in addition to policy interventions.

Respondents acknowledged the influence of cultural factors on STEM participation, particularly regarding societal expectations and perceptions of intelligence. This aligns with the findings of Williams and Shipley's [45], who recognized the impact of cultural norms on career choices in STEM, echoing the work of Kricorian et al. [46]. While previous research has addressed cultural factors in STEM participation, this study adds a focus on familial and community pressures, highlighting the importance of support systems within families and communities. This suggests a more holistic approach to addressing cultural barriers in STEM education.

Respondents emphasized the importance of community engagement and stakeholder collaboration in promoting gender equity in STEM education. This aligns with Allen et al. [47] and Appel et al. [48], who stress the need for robust community engagement to dismantle barriers. However, while previous research emphasizes the importance of community engagement, this study adds a focus on collaborative efforts between schools, businesses, and industries, suggesting a broader approach to stakeholder collaboration in promoting gender equity in STEM education.


**1. Introduction**

The definition of biomass may vary due to a number of factors such as the heterogeneity of biomass, its application and origin [1]. However, any organic material directly or indirectly derived from the process of photosynthesis is considered biomass. Generally, biomass is a biological material that is present in various forms such as *wood*, *sugarcane bagasse*, *rice husk*, *rice straw*, *corn cob*, *paper waste*, *municipal solid waste*, *animal manure*, *sewage*, *algae*, *etc*. Traditionally, the use of biomass resources is becoming increasingly vital because of their substantial annual volumes and great economic potential. The main application of biomass is as a source of energy, and it has been reported that global biomass land and aquatic reserves stand at around 1.8 trillion tons for biomass, and about 4 billion tons for aquatic reserves, respectively [1]. In spite of these huge reserves, biomass remains under exploited, accounting for only about 14% of global energy with ca. 56 million TJ/year [1]. This is because the application of biomass as a source of energy has not shown a homogeneous distribution across the globe. In some developed countries,

energy production from biomass averages about 11% of the total energy produced; while in developing countries this can be up to 50% [2, 3]. For example, Europe generates about 3.5% of its energy from biomass, which is ca. 40 Mtoe/year; while countries like Austria, Finland and Sweden have about 13, 18 and 17% of their total energy produced from biomass resources; the United States on the other hand generates 3% of its energy from biomass [4]. In general, there is a huge potential for the exploitation of biomass as a source of energy because some countries in central and northern Europe have actually established large plants for heat and power production from biomass. However, there are two main routes by which biomass can be converted to energy and bio products. These are biochemical and thermochemical conversion routes. While the former involves breaking down biomass into gaseous and/or liquid fuels (such as biogas and bioethanol) through the use of bacteria, microorganisms and enzymes; the latter involves the use of heat to disrupt the complex chemical structure of biomass (particularly lignocellulosic biomass such as wood) into various products that includes heat, power, fuels, bio oil, biochar and chemicals [5, 6].

of both pretreated and non-pretreated biomass relevant to the production of fuels and chemicals. It is believed that improving the fundamental knowledge of biomass pretreatment and characterization will lead to significant advances in the field of

The composition of biomass is largely diverse and dependent upon its origin and species. Besides plant biomass, which are commonly referred to as lignocellulosic biomass (LCB) due to their three major cross-linked polysaccharide constituents (cellulose, hemicellulose and lignin), there are other biomass materials whose primary components include *lipids*, *proteins*, *starch*, *inorganics and minerals*. These types of biomass materials are regarded as non-lignocellulosic biomass (NLCB) and include *sewage sludge*, *animal manure*, *algae*, etc. The major constituents of both the LCB and NLCB are organic in nature and determine the characteristics of the entire biomass [7]. In comparison to LCB however, NLCB pose a greater threat to the environment because of higher content of heavy metals and heteroatoms such as nitrogen (N) and phosphorus (P); [11–13]. The heavy metals can pollute water systems, accumulate in food chains and cause serious health issues [14, 15]. Even though the N and P composition of NLCB can serve as a source of nutrients for plants, excessive amounts of these elements can lead to eutrophication of a water body, a condition linked to the deterioration of water quality via excessive growth of algae and other aquatic plants, resulting in oxygen depletion of the water body,

While the structural unit of NLCB is such that various atoms are arranged in an

orderly manner, LCB is characterized by complex internal structure with main components that equally displays structural multiplicities. **Figure 1** shows the internal structure of lignocellulosic biomass and how its three primary components

The internal structure of lignocellulosic biomass reveals a crystalline fibrous structure of cellulose, which forms the core of the complex structure of plant biomass. Positioned between the micro- and macrofibrils of the cellulose matrix is hemicellulose; while lignin plays a structural role that encapsulates both cellulose

The composition of biomass varies significantly depending on the source of the biomass. In addition to the three primary components of LCB (**Figure 1**), there are other minor components such as *extractives*, *proteins*, *water* and *inorganic components*

*A structural representation of lignocellulosic biomass. Reproduced with permission from [1].*

sustainable energy and chemicals production from biomass.

**2. Overview of biomass composition**

*Biomass Pretreatment and Characterization: A Review DOI: http://dx.doi.org/10.5772/intechopen.93607*

leading to the death of aquatic animals [12].

are distributed.

and hemicellulose.

**Figure 1.**

**21**

It is evident from the above that the application of biomass as a source of energy continues to attract global attention even in the midst of its gross underutilization. The low quality of biomass, often defined in terms of its inherent characteristics (high moisture content, low energy density, low bulk density, irregular size and shape) has been one of the major reasons for its under exploitation [7]. In other words, biomass in its natural form is difficult to use for the purpose of energy production hence pretreatment is often required to overcome its recalcitrant nature and make the biomass amenable for conversion through either of the conversion routes previously mentioned for maximum product recovery; the pretreatment leads to physical, chemical and structural changes to the biomass plays a vital role in the commercial viability of the energy production process of biomass [7–9]. Thus, bioconversion and bio refinery interests define the type of pretreatment measures for biomass [7, 10]. However, there are different categories of pretreatment viz; physical, chemical and biological pretreatments. These three classes of pretreatment are described in greater detail in Section 3.

Another critical step to the optimization of biomass conversion and bio refinery processes is related to the characterization of biomass to determine its suitability for the aforementioned conversion processes. This means that the effectiveness and impact of pretreatment on biomass can be determined through the use of a variety of high-quality analytical techniques able to provide information on quintessential biomass characteristics that can be used to maximize product recovery. Depending on the end application of biomass, some of the useful characterization techniques include *atomic force microscopy (AFM)*, *Fourier transform infrared spectroscopy (FT-IR)*, *scanning electron microscopy (SEM)*, *transmission electron microscopy (TEM)*, *X-ray diffraction (XRD)*, *solid state nuclear magnetic resonance (SSNMR)*, to name a few. An overview of these analytical tools is presented in subsequent sections. For a full understanding of the characteristics of biomass, its value and information for the design and operation of the energy conversion systems using the biomass as feedstock, it is vital to undertake biomass characterization before and after pretreatment. Due to the complex nature of biomass, the study of the mechanisms involved in its conversion process to energy and bio products is quite challenging. The lack of rapid, high throughput and reliable tools for assessing and tracing biomass components relevant not just to energy production but also to other value added products remains a major bottleneck in studying the impact of biomass pretreatment and process parameters. This chapter therefore presents a critical review of biomass pretreatment and characterization and discusses the applications of state-of-the-art analytical techniques commonly used to understand the features

of both pretreated and non-pretreated biomass relevant to the production of fuels and chemicals. It is believed that improving the fundamental knowledge of biomass pretreatment and characterization will lead to significant advances in the field of sustainable energy and chemicals production from biomass.