Unconventional resources such as shale gas and tight oil are contributing more and more significantly in the energy nexus. However, porosity and permeability of these reservoirs are extremely low; therefore, stimulating technologies are required. The state-of-the-art solution for such a target is water fracturing, but its application suffers from massive water usage and related environmental issues. As a greener alternative, fracturing with CO 2 may bring multiple benefits, including effective fracturing, enhanced recovery, carbon storage, and others.

A series of highly ordered hierarchically porous silica and bio-glasses materials with macropore size of 8-1,000 lm and mesopore size of 3.1-5.6 nm have been synthesized using six plant based materials as templates. However, the as-obtained porous structure was reported for the first time with interconnected 3D macropore up to 1,000 lm. The porous silica materials were used as the host for drug loading and release, which showed a good sustained delivery function. The as-synthesized bio-glasses materials indicated the highly bioactive capability in the bone regeneration. This method can be utilized to synthesize other multi-porous bioactive glasses using different plants as templates for bone tissue repairing.

Traffic volume data is one of the most critical variables for signal retiming. However, collecting traffic volume manually can be time-consuming and costly. In recent years, video-based sensor systems have been applied on signalized intersections for signal timing control. The detectors in video-based sensors generate large amounts of real-time high-resolution event-based data, including signal status and detection status data. The vehicle arrivals for each detection event is a stochastic process and has a relationship with the signal status and the detection duration (time occupancy). Therefore, a modified dynamic hidden Markov model (DHMM) is proposed to estimate vehicular volume by modeling the vehicle arrivals using event-based data collected at signalized intersections. The concept of an additional hidden state is introduced to make the vehicular volume finite by grouping volumes that have only a small probability of occurring into one hidden state. Additionally, a linear regression model is built to estimate the vehicular volume when the output of the DHMM is an additional hidden state. The resulting mean absolute percentage errors of the 15-min estimated volume are 14.1%, 10.3%, and 10.5%, respectively, at three study locations in Tucson, Arizona.

This paper proposes a closed-loop identification method for the design of a robust PID controller of the turbine control loop in the boiler-turbine coordinated control system. The process model is firstly identified. In the identification procedure, set-point change tests are adopted to calculate the model based on process inputs and outputs in control loops. The process frequency-response matrix is estimated, and then a transform function matrix is obtained by least square method. The robust PID controller of the turbine control loop is designed by using the internal model control (IMC) method based on the obtained model. It is shown that the IMC-PID controller maintains the robust performance and minimizes the effects of the external disturbance in power system or the internal disturbances in the power plant.