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Understanding scale-up effects on the hydrodynamics of a liquid‒solid circulating fluidized bed (LSCFB) unit requires both experimental and theoretical analysis. We implement multigene genetic programming (MGGP) to investigate the solid holdup and distribution in three LSCFB systems with different heights. In addition to data obtained here, we also use a portion of data sets of LSCFB systems developed by Zheng (1999) and Liang et al. (1996) . Model predictions are in good agreement with the experimental data in both radial and axial directions and at different normalized superficial liquid and solid velocities. The radial profiles of the solid holdup are approximately identical at a fixed average cross-sectional solid holdup for the three LSCFB systems studied. Statistical performance indicators including the mean absolute percentage error (6.19%) and correlation coefficient (0.985) are within an acceptable range. The results suggest that a MGGP modeling approach is suitable for predicting the solid holdup and distribution of a scaled-up LSCFB system.

This communication presents the application of Multigene Genetic Programming, a new soft computing technique to investigate the effects of particle size on hydrodynamics behavior of a liquid–solid circulating fluidized bed (LSCFB) riser. The Multigene Genetic Programming based model is developed/trained based on experimental data collected from a pilot scale LSCFB reactor using two different size glass beads (500 & 1200 μm) as solid phase and water as liquid phase. The trained Genetic Programming model successfully predicted experimental phase holdups of the LSCFB riser under different operating parameters. It is observed that the model predicted cross-sectional average of solids holdups in the axial directions and radial flow structure are well agreement with the experimental values. The statistical performance indicators including the mean absolute error (∼5.89%) and the correlation coefficient (∼0.982) also show favorable indications of the suitability of Genetic Programming modeling approach in predicting the solids holdup of the LSCFB system.

The world is experiencing a tremendous growth in transportation sector which is also prevalent in Saudi Arabia. This phenomenon results in an increasing demand for fossil fuel for both national and international transportation. This study presents the (i) analysis of the historical fossil fuel energy consumption trend, (ii) forecasted energy consumption using double exponential smoothing method, (iii) study of estimated and projected emissions of greenhouse gas (GHG), (iv) factors influencing GHG emissions, (v) potential mitigation measures, and (vi) mitigation initiatives to reduce GHG emissions from the road transportation sector of the Kingdom. This study revealed that the per capita fuel consumption in Saudi Arabia is increasing at higher rates in recent years compared to some other neighboring countries along with the consistent increase in number of cars and population growth. As a result, the domestic fuel consumption is growing significantly and the growth dynamics of GHG emissions is becoming quite challenging for planning, development, and implementation of appropriate mitigation measures. An integrated national effort with strong commitments from all the stakeholders is a strategic imperative for the Kingdom to ensure its rapid development and successful implementation of commendable national policy for reducing the emission of GHG, particularly from the road transportation sector. Success of the Kingdom’s efforts to manage GHG emissions from the road transportation sector while maintaining its ambitious development stride and commitments to a stable global energy supply demands increased and comprehensive focus on vehicle efficiency, environment friendly fuels, and management of growing travel demand considering the specific socio-economic characteristics.

This study conducted a survey of traffic crashes with the data collected from police stations in the three cities of Saudi Arabia involving different features related to crashes, drivers, vehicles, and understanding of traffic signs. Among the chauffeurs, drivers at fault and not at fault were separated and investigated through factor analysis for 19 parameters related to their background and knowledge of traffic signs. The data show that a particular age group and time of the day may provide more insights to characterize the overall crashes in these cities. The factor analysis shows that the drivers at fault and not at fault may have distinguishable profile. Logit models were developed to quantify the effects of these variables. The models show that driver’s experience and knowledge of traffic signs for chauffeurs has positive impact on reducing faulty behavior of drivers. Approximately, 68%–74% of the original variables are required to characterize chauffeurs, indicating the possibility of data reduction in traffic safety monitoring program. This study may assist in profiling the chauffeurs involved in crashes and reducing the parameters to be monitored for traffic safety program. The recommendations of this study may be considered beneficial in making policy for licensing and hiring of chauffeurs.

Power system networks are the largest and most complex systems ever devised by human being. The networks are associated with huge amount of investments where the success of the sector heavily depends on appropriate planning and management. Spatial analysis technologies play very important role in planning, monitoring, and managing of the network by comprehensively considering social, environmental, and economic issues. Spatial analysis, the heart of geographic information systems (GIS) provide an integrated platform for proper management and planning of power systems. The major role of GIS technology includes: (i) developing spatial model for power generating stations, transmission networks, and distribution substations; (ii) determining suitable locations for power generation and distribution stations, and optimal routing of transmission networks; and (iii) integrating renewable energy resources with the planning and management system. Parallel to GIS, global positioning systems (GPS) has introduced new dimensions in spatial research because of its increasing availability with reduced price. The integration of GPS in data measurement techniques made a paradigm shift in (i) monitoring of the power system network with time synchronized data and (ii) finding fault locations as well as taking corrective actions with better accuracy. This paper investigates the evolutionary role of GIS and GPS technologies in different components of power system networks. These technologies are expected to provide a smart and promising platform for integrating virtually all the relevant information and systems required to develop and maintain a sustainable power system networks at local, national, regional, and global levels.

The largest contributor of greenhouse gas (GHG) emission in the United Arab Emirates (UAE) is the energy sector. More than 90% of the total GHG is emitted from this sector. The rapid rise of population, high rate of urbanization, rapid economic growth, and low energy cost increase the demand for energy. The consistently increasing trend of energy consumption and GHG emissions pose a challenge for the country. This study has (i) investigated the major sources of energy consumption and GHG emissions, (ii) analyzed the growth pattern of the source categories, (iii) forecasted the GHG emissions under the business-as-usual and the reformed scenarios, (iv) synthesized widely varied initiatives of the UAE in GHG emission mitigation, (v) highlighted the challenges in deploying renewable energy resources, and (vi) discussed on the possible GHG mitigation approaches. The findings of this study will contribute in preparing national GHG emissions inventory, investigating the dynamics of national GHG emissions with particular reference to the energy sector, developing possible future energy outlook scenarios, and selecting appropriate policy measures to mitigate GHG emissions.

Corrosion initiation time of embedded steel is an important service life parameter, which depends on concrete material make-up, exposure environment, and duration of exposure. Early and accurate determination of corrosion initiation time will aid in designing durable reinforced concrete, saves cost and time. This study leveraged on the power of ensemble machine learning by combining the performances of different models in estimating the corrosion initiation time of steel embedded in self compacted concrete using corrosion potential measurement. The concrete specimens were prepared with limestone powder as supplementary addition to Portland cement and was exposed to 5% sodium chloride in accordance with the requirements of ASTM C876 – 15 for 8 months. During the exposure, corrosion potential of the embedded steel was measured, and the recorded datasets were used in training five different machine learning models. With cement, limestone powder, coarse aggregate, fine aggregate, water and exposure period.as input variables, five different models were developed to estimate the corrosion initiation time (determined from the corrosion potential measurements) of the embedded steel. With respect to model predictive performance, the acquired results demonstrated that the random forest (RF) ensemble model amongst other trained models performed best with 85/15 dataset percentage split for the training and testing. RF ensemble performed best with CC and RMSE of 99.01% and 18.2747 mV for training, and 98.67% and 25.0298 mV for testing respectively. Hence, due to its superior and robust performance, this study proposes RF ensemble model in the estimation of corrosion initiation time of embedded steel in reinforced limestone-cement blend concrete.

Low-frequency oscillations should be dealt with extreme care for secure electric networks. This paper tunes the critical parameters of power system stabilizers in three different electric networks in real-time, employing the neurogenetic approach to damp out the low-frequency oscillations. The first network is a single machine infinite bus power system equipped with a power system stabilizer. Besides, the second and third networks are coordinated with second-generation flexible alternating current transmission system devices, namely a unified power flow controller and static synchronous compensator in coordination with power system stabilizers, respectively. The investigation of eigenvalue and minimum damping ratio analyses for different loading conditions proves the efficiency of the proposed approach. Additionally, the time-domain simulation comparison shows the superiority of the proposed approach over the conventional method. Besides, the satisfactory values of the statistical performance measures give confidence to the proposed approach in predicting power system stabilizer parameters and thus mitigating the low-frequency oscillations in real-time.

Climate change mitigation in rapidly growing developing countries is receiving increased global attention, especially after the 2016 Paris Agreement. In Bangladesh, the government has initiated some climate change policies to reduce its greenhouse gas emissions in the last decade. However, the rate of emissions has been increasing recently, which questions the emissions reduction potential of these policies. Therefore, this study has utilized relevant indicators to evaluate the extent to which the policies are mitigating climate change, and of their synergy with national development goals. The findings indicate that the policies have limited impacts on energy decarbonization, energy demand management, and emissions sink improvement. Although policies on renewable energy, energy efficiency and demand management, and afforestation are aligned with national development goals, the policies on electricity generation from coal and forest biomass have little synergy with the goals. By quantitatively evaluating the effects of its climate change policies in enhancing socioeconomic, and environmental benefits, this study can assist Bangladesh’s policymakers to understand the impact of the policies on climate change mitigation and their level of integration with national development goals. Also, other developing countries can adopt the study methodology to evaluate their climate change policies and track their progress.

In arid regions primary pollutants contribute to the increase of ozone levels, which cause negative effects on biotic health. This study investigates the use of abductive networks based on the group method data handling (GMDH) for ozone prediction. Abductive network models are automatically synthesized from a database of inputs and outputs. The models are developed for a location in the Empty Quarter, Saudi Arabia, first using only the meteorological data and derived meteorological data. In the subsequent efforts, NO and NO 2 concentrations and their transformations were incorporated as additional inputs. Another model forecasted ozone level after 1 h using mainly meteorological data, NO, and NO 2 concentrations. Models built for specific period of day are simpler compared to the generic models. Finally, ensemble modeling approach was also investigated. A time specific model produced mean absolute percentage error (MAPE) of 3.82%. The proposed models are self-organizing in nature and require less intervention from the users, and can be implemented easily by the interested practitioners.

An artificial neural network (ANN) approach is investigated to model and study the phase holdup distributions of a liquid–solid circulating fluidized bed (LSCFB) system. The ANN model is developed based on different operating parameters of the LSCFB including primary and auxiliary liquid velocities, and superficial solids velocity. The competency of the model is examined by comparing the model predicted and the experimental phase holdup of the LSCFB riser reactor. It is also found that the ANN model successfully predicted the radial non-uniformity of phase holdup that is observed in the experimental runs of the riser. When compared, the model predicted output and trend of radial flow structure for solids holdup are in well agreement with the experiments. The mean absolute percentage error is around 6% and the correlation coefficient value of the predicted output and the experimental data is 0.992.

The main causes of global warming are now attributed to the burning of fossil fuels. Saudi Arabia is the world's largest producer and exporter of total petroleum liquids, and one of the largest consumers of total primary energy. The activities which are mainly responsible for significant greenhouse gas emissions are consistently in the upslope. The electricity generation, the solid waste management, and the agricultural sectors are responsible for the highest share of emissions of CO 2 , CH 4 , and N 2 O, respectively. The results of current research provided the initial justifications for the renewable energy sources such as solar and wind energy conversion, and hybrid systems. The Master Gas Collection System of Saudi Aramco can be considered as a remarkable step forward in lowering CH 4 emissions from the oil and gas fields. The integrated efforts of the public and private sectors are essential for development and implementation of appropriate strategies to reduce greenhouse gas emissions. The study provides an overview of Saudi initiatives related to policy, plan, program, and/or project towards the reduction of greenhouse gases and enhancement of carbon capture and storage.

Wind energy has become a major competitor of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, wind with reasonable speed is not adequately sustainable everywhere to build an economical wind farm. The potential site has to be thoroughly investigated at least with respect to wind speed profile and air density. Wind speed increases with height, thus an increase of the height of turbine rotor leads to more generated power. Therefore, it is imperative to have a precise knowledge of wind speed profiles in order to assess the potential for a wind farm site. This paper proposes a clustering algorithm based neuro-fuzzy method to find wind speed profile up to height of 100 m based on knowledge of wind speed at heights 10, 20, 30, 40 m. The model estimated wind speed at 40 m based on measured data at 10, 20, and 30 m has 3% mean absolute percent error when compared with measured wind speed at height 40 m. This close agreement between estimated and measured wind speed at 40 m indicates the viability of the proposed method. The comparison with the 1/7th law and experimental wind shear method further proofs the suitability of the proposed method for generating wind speed profile based on knowledge of wind speed at lower heights.