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Prof. Ali Elkamel
Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

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Research Keywords & Expertise

0 Combinatorial Optimization
0 Energy and environmental engineering systems
0 Air pollution modeling, simulation and control
0 Sustainable development, planning and scheduling of process operations
0 Dynamic modeling and optimization

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Energy and environmental engineering systems

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Correction
Published: 17 August 2021 in Biomass Conversion and Biorefinery
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ACS Style

Hesham Alhumade; Muhammad Sajjad Ahmad; Emanuele Mauri; Yusuf Al-Turki; Ali Elkamel. Correction to: Investigating the bioenergy potential of invasive Reed Canary (Phalaris arundinacea) through thermal and kinetic analyses. Biomass Conversion and Biorefinery 2021, 1 -1.

AMA Style

Hesham Alhumade, Muhammad Sajjad Ahmad, Emanuele Mauri, Yusuf Al-Turki, Ali Elkamel. Correction to: Investigating the bioenergy potential of invasive Reed Canary (Phalaris arundinacea) through thermal and kinetic analyses. Biomass Conversion and Biorefinery. 2021; ():1-1.

Chicago/Turabian Style

Hesham Alhumade; Muhammad Sajjad Ahmad; Emanuele Mauri; Yusuf Al-Turki; Ali Elkamel. 2021. "Correction to: Investigating the bioenergy potential of invasive Reed Canary (Phalaris arundinacea) through thermal and kinetic analyses." Biomass Conversion and Biorefinery , no. : 1-1.

Original article
Published: 01 July 2021 in Biomass Conversion and Biorefinery
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The thermal conversion of biomass plays an important role in the development of energy reaping technologies and fire engineering. The study investigates the bioenergy potential of Reed Canary (Phalaris arundinacea) through investigating the combustion kinetics and thermal behavior. Reed Canary samples were collected from various rural areas of Ontario, Canada. Four heating rates (10, 20, 30, and 40 °C min−1) were utilized to perform the thermal degradation analysis using a thermogravimetric analyzer. Three different stages were identified ranging from 25 °C to 800 °C in which major degradation stage had two regions from 210 °C to 530 °C where most of the biomass changed into products. Furthermore, iso-conversional models including Kissenger-Akahira-Sunose (KSA), Starink and Flynn–Wall–Ozawa (FWO) were used to evaluate the reaction kinetics such as the activation energy and the pre-exponential factor. The reported kinetics parameters demonstrate the promising potential of Reed Canary for bioenergy production. Moreover, the low cost and the abundance of Reed Canary facilitate the possibility of introducing the biomass as a cost efficient and environmentally friendly natural resource for renewable bioenergy production.

ACS Style

Hesham Alhumade; Muhammad Sajjad Ahmad; Emanuele Mauri; Yusuf Al-Turki; Ali Elkamel. Investigating the bioenergy potential of invasive Reed Canary (Phalaris arundinacea) through thermal and kinetic analyses. Biomass Conversion and Biorefinery 2021, 1 -9.

AMA Style

Hesham Alhumade, Muhammad Sajjad Ahmad, Emanuele Mauri, Yusuf Al-Turki, Ali Elkamel. Investigating the bioenergy potential of invasive Reed Canary (Phalaris arundinacea) through thermal and kinetic analyses. Biomass Conversion and Biorefinery. 2021; ():1-9.

Chicago/Turabian Style

Hesham Alhumade; Muhammad Sajjad Ahmad; Emanuele Mauri; Yusuf Al-Turki; Ali Elkamel. 2021. "Investigating the bioenergy potential of invasive Reed Canary (Phalaris arundinacea) through thermal and kinetic analyses." Biomass Conversion and Biorefinery , no. : 1-9.

Journal article
Published: 26 June 2021 in Membranes
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Oil and gas industries produce a huge amount of wastewater known as produced water which contains diverse contaminants including salts, dissolved organics, dispersed oils, and solids making separation and purification challenging. The chemical and thermal stability of graphene oxide (GO) membranes make them promising for use in membrane pervaporation, which may provide a more economical route to purifying this water for disposal or re-use compared to other membrane-based separation techniques. In this study, we investigate the performance and stability of GO membranes cast onto polyethersulfone (PES) supports in the separation of simulated produced water containing high salinity brackish water (30 g/L NaCl) contaminated with phenol, cresol, naphthenic acid, and an oil-in-water emulsion. The GO/PES membranes achieve water flux as high as 47.8 L m−2 h−1 for NaCl solutions for membranes operated at 60 °C, while being able to reject 99.9% of the salt and upwards of 56% of the soluble organic components. The flux for membranes tested in pure water, salt, and simulated produced water was found to decrease over 72 h of testing but only to 50–60% of the initial flux in the worst-case scenario. This drop was concurrent with an increase in contact angle and C/O ratio indicating that the GO may become partially reduced during the separation process. Additionally, a closer look at the membrane crosslinker (Zn2+) was investigated and found to hydrolyze over time to Zn(OH)2 with much of it being washed away during the long-term pervaporation.

ACS Style

Khalfan Almarzooqi; Mursal Ashrafi; Theeran Kanthan; Ali Elkamel; Michael Pope. Graphene Oxide Membranes for High Salinity, Produced Water Separation by Pervaporation. Membranes 2021, 11, 475 .

AMA Style

Khalfan Almarzooqi, Mursal Ashrafi, Theeran Kanthan, Ali Elkamel, Michael Pope. Graphene Oxide Membranes for High Salinity, Produced Water Separation by Pervaporation. Membranes. 2021; 11 (7):475.

Chicago/Turabian Style

Khalfan Almarzooqi; Mursal Ashrafi; Theeran Kanthan; Ali Elkamel; Michael Pope. 2021. "Graphene Oxide Membranes for High Salinity, Produced Water Separation by Pervaporation." Membranes 11, no. 7: 475.

Journal article
Published: 27 May 2021 in Computers & Chemical Engineering
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With the increasing demand for complicated control and monitoring systems in chemical process plants based on tremendous improvements in the digital world, the number of devices for monitoring and controlling the plants has risen dramatically. In such huge networks of a myriad of instrument devices, determination of an optimum proactive maintenance interval (PMI) has become vital for enhancing plant safety and reducing maintenance costs. PMI optimization is a matter of considerable benefit variation, process reliability, personnel safety, and environmental concerns. In this paper, a comprehensive formula and a concise formula are developed and applied for obtaining the optimum PMI based on the expected utility theory. The proposed formulas are applicable for determining the most affordable and effective maintenance plan providing the lowest cost and acceptable reliability, availability, and safety in oil and gas industries in terms of an optimal decision-making problem. By considering variable failure rates and components' wear and tear, a comprehensive formula has been developed. Moreover, for constant failure rate and returning to the “as-new” condition after each proactive maintenance, a concise and “easy-to-use” formula has been obtained. Finally, it has been shown through practical implementation of the presented methodology in a typical gas refinery that about 41% benefit is achieved in one year, compared with the previous heuristic maintenance strategy in the same duration.

ACS Style

Reza Abbasinejad; Farzad Hourfar; Ali Elkamel. Optimum Maintenance Interval Determination for Field Instrument Devices in Oil and Gas Industries Based on Expected Utility Theory. Computers & Chemical Engineering 2021, 152, 107362 .

AMA Style

Reza Abbasinejad, Farzad Hourfar, Ali Elkamel. Optimum Maintenance Interval Determination for Field Instrument Devices in Oil and Gas Industries Based on Expected Utility Theory. Computers & Chemical Engineering. 2021; 152 ():107362.

Chicago/Turabian Style

Reza Abbasinejad; Farzad Hourfar; Ali Elkamel. 2021. "Optimum Maintenance Interval Determination for Field Instrument Devices in Oil and Gas Industries Based on Expected Utility Theory." Computers & Chemical Engineering 152, no. : 107362.

Review article
Published: 26 April 2021 in Frontiers in Chemical Engineering
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The global trend toward a green sustainable future encouraged the penetration of renewable energies into the electricity sector to satisfy various demands of the market. Successful and steady integrations of renewables into the microgrids necessitate building reliable, accurate wind and solar power forecasters adopting these renewables' stochastic behaviors. In a few reported literature studies, machine learning- (ML-) based forecasters have been widely utilized for wind power and solar power forecasting with promising and accurate results. The objective of this article is to provide a critical systematic review of existing wind power and solar power ML forecasters, namely artificial neural networks (ANNs), recurrent neural networks (RNNs), support vector machines (SVMs), and extreme learning machines (ELMs). In addition, special attention is paid to metaheuristics accompanied by these ML models. Detailed comparisons of the different ML methodologies and the metaheuristic techniques are performed. The significant drawn-out findings from the reviewed papers are also summarized based on the forecasting targets and horizons in tables. Finally, challenges and future directions for research on the ML solar and wind prediction methods are presented. This review can guide scientists and engineers in analyzing and selecting the appropriate prediction approaches based on the different circumstances and applications.

ACS Style

Hanin Alkabbani; Ali Ahmadian; Qinqin Zhu; Ali Elkamel. Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review. Frontiers in Chemical Engineering 2021, 3, 1 .

AMA Style

Hanin Alkabbani, Ali Ahmadian, Qinqin Zhu, Ali Elkamel. Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review. Frontiers in Chemical Engineering. 2021; 3 ():1.

Chicago/Turabian Style

Hanin Alkabbani; Ali Ahmadian; Qinqin Zhu; Ali Elkamel. 2021. "Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review." Frontiers in Chemical Engineering 3, no. : 1.

Journal article
Published: 25 April 2021 in Energies
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The enrichment of natural gas with hydrogen has been identified as a promising pathway for power-to-gas technology with the potential to reduce emissions while achieving feasible return on investment. The evolving regulatory market in the province of Ontario motivates the analysis of business cases for hydrogen on the industrial microgrid scale. This paper aims to investigate the financial and environmental returns associated with producing and storing electrolytic hydrogen for injection into the natural gas feed of a manufacturer’s combined heat and power plants (CHPs). A mathematical methodology was developed for investigating the optimal operation of the integrated system (power-to-gas along with the current system) by considering hydrogen-enriched natural gas. The result of this simulation is an operation plan that delivers optimal economics and an estimate of greenhouse gas emissions. The simulation was implemented across an entire year for each combination of generation price limit and storage coefficient. Because the provincial grid imposes a lesser carbon footprint than that of a pure natural gas turbine, any offset of natural gas by hydrogen reduces the carbon intensity of the system. From an environmental perspective, the amount of carbon abated by the model fell within a range of 3000 ton CO2/year. From a policy perspective, this suggests that a minimum feasible carbon price of $60/ton CO2e must be set by applicable regulatory bodies. Lastly, a Failure Modes and Effects Analysis was performed for the proposed system to validate the safety of the design.

ACS Style

Nicholas Preston; Azadeh Maroufmashat; Hassan Riaz; Sami Barbouti; Ushnik Mukherjee; Peter Tang; Javan Wang; Ali Elkamel; Michael Fowler. An Economic, Environmental and Safety Analysis of Using Hydrogen Enriched Natural Gas (HENG) in Industrial Facilities. Energies 2021, 14, 2445 .

AMA Style

Nicholas Preston, Azadeh Maroufmashat, Hassan Riaz, Sami Barbouti, Ushnik Mukherjee, Peter Tang, Javan Wang, Ali Elkamel, Michael Fowler. An Economic, Environmental and Safety Analysis of Using Hydrogen Enriched Natural Gas (HENG) in Industrial Facilities. Energies. 2021; 14 (9):2445.

Chicago/Turabian Style

Nicholas Preston; Azadeh Maroufmashat; Hassan Riaz; Sami Barbouti; Ushnik Mukherjee; Peter Tang; Javan Wang; Ali Elkamel; Michael Fowler. 2021. "An Economic, Environmental and Safety Analysis of Using Hydrogen Enriched Natural Gas (HENG) in Industrial Facilities." Energies 14, no. 9: 2445.

Journal article
Published: 22 April 2021 in Computers & Chemical Engineering
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With growing research interest in renewable energy generation and storage technologies, a need arises for a framework integrating renewable energy within the process industry. In this study, the multi-energy hub approach was used to develop a model to achieve economic gains and carbon emissions reduction. Furthermore, a case study on a refinery was carried out to investigate the applicability of the proposed model. Lowest carbon emissions were realized when utilizing wind coupled with concentrated solar power technologies for electricity and heat generation, respectively. This configuration, for the particular refinery case study, mitigated about 9.8 ktonnes carbon dioxide emissions at an additional annual cost of about $88,000, as compared to the assumed case of utilizing grid energy. Whilst considering energy storage, a further reduction of 1.94 ktonnes of carbon emissions can be attained by employing 9.8 MWh of thermal energy storage, at an added annual cost of $275,000. Moreover, different scenarios were investigated to study the impact of schemes such as carbon cap-and-trade and carbon capture and storage on economic costs and carbon dioxide emissions. Under the carbon cap-and-trade scenario, the lowest annual cost while minimizing annual carbon dioxide emissions was realized when a high carbon credit value of $ 0.00014 gCO2−1 was utilized. A Pareto front was generated, outlining the optimal cost and carbon emissions when employing different configurations and storage technologies.

ACS Style

Syed Taqvi; Ali Almansoori; Ali Elkamel. Optimal renewable energy integration into the process industry using multi-energy hub approach with economic and environmental considerations: Refinery-wide case study. Computers & Chemical Engineering 2021, 151, 107345 .

AMA Style

Syed Taqvi, Ali Almansoori, Ali Elkamel. Optimal renewable energy integration into the process industry using multi-energy hub approach with economic and environmental considerations: Refinery-wide case study. Computers & Chemical Engineering. 2021; 151 ():107345.

Chicago/Turabian Style

Syed Taqvi; Ali Almansoori; Ali Elkamel. 2021. "Optimal renewable energy integration into the process industry using multi-energy hub approach with economic and environmental considerations: Refinery-wide case study." Computers & Chemical Engineering 151, no. : 107345.

Editorial
Published: 08 March 2021 in Pollutants
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Pollutants (ISSN 2673-4672) is an international, peer-reviewed open access journal focusing on contaminants that are introduced into the natural environment, beyond permitted limits, and cause measurable deleterious effects on air, water, soil, or living organisms

ACS Style

Ali Elkamel. Pollutants—Focus on Solving Environmental Pollution Problems. Pollutants 2021, 1, 65 -65.

AMA Style

Ali Elkamel. Pollutants—Focus on Solving Environmental Pollution Problems. Pollutants. 2021; 1 (1):65-65.

Chicago/Turabian Style

Ali Elkamel. 2021. "Pollutants—Focus on Solving Environmental Pollution Problems." Pollutants 1, no. 1: 65-65.

Journal article
Published: 25 February 2021 in Pollutants
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Biosorption of chromium (Cr(VI)) is studied by using raw (chemically not modified) Moringa (Moringa Oleifera) leaf powder without any pretreatment. Cr(VI) is one of the potentially harmful heavy metals found in industrial wastewater. In the Moringa leaf powder, the presence of a significant amount of organic acids form the source for the biosorption of Cr(VI). The concentration of Cr(VI) in the feed solution is varied and different dosages of the proposed biosorbent are used to study its efficiency in the removal of Cr(VI). The concentration of Cr(VI) is varied from 1 ppm to 20 ppm while the amount of biosorbent is varied from 0.5 g to 2.5 g. The equilibrium time for adsorption of Cr(VI) is observed to vary between half an hour and 90 min. The metal removal efficiency varied from 30% to 90% which is a significant achievement compared to other conventional methods which are either energy-intensive or not cost effective. The experimental results are modeled using Langmuir, Freundlich and Redlich–Peterson isotherms. The metal removal efficiency is attributed to the chelating effect of carboxylate and hydroxyl groups present in the moringa leaves and is confirmed from the FTIR analysis. Further molecular docking simulations are performed to confirm the binding of the metal to the speculated sites within the different acids present in the moringa leaves. Untreated green moringa leaf powder used as a biosorbent in this study leads to a sustainable and cheaper option for treating wastewater containing Cr(VI).

ACS Style

Chandra Madhuranthakam; Archana Thomas; Zhainab Akhter; Shannon Fernandes; Ali Elkamel. Removal of Chromium(VI) from Contaminated Water Using Untreated Moringa Leaves as Biosorbent. Pollutants 2021, 1, 51 -64.

AMA Style

Chandra Madhuranthakam, Archana Thomas, Zhainab Akhter, Shannon Fernandes, Ali Elkamel. Removal of Chromium(VI) from Contaminated Water Using Untreated Moringa Leaves as Biosorbent. Pollutants. 2021; 1 (1):51-64.

Chicago/Turabian Style

Chandra Madhuranthakam; Archana Thomas; Zhainab Akhter; Shannon Fernandes; Ali Elkamel. 2021. "Removal of Chromium(VI) from Contaminated Water Using Untreated Moringa Leaves as Biosorbent." Pollutants 1, no. 1: 51-64.

Research article
Published: 25 November 2020 in International Journal of Energy Research
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Processing natural gas, as a widely used source of energy in our life, is imperative to eliminate the impurities in order to make it consumable. So, appropriate modeling of different units in a real gas processing plant (GPP) is an essential research field. Moreover, high‐dimensional data, with probably unnecessary information, gathered from a real application may lead to complicated models. As a result, the original dataset, obtained through a three‐level design of experiments, should be refined to achieve the most effective observations in a lower dimension vector space. On the other hand, the original dataset needs to be normalized to a standard normal distribution in order to tune the effects of all the variables on the system operation. In this study a radial basis function‐neural network (RBF‐NN) is designed to model the total consumed energy in separation, sweetening, and dehydration units and also the water content in the refined gas in a typical GPP, using a reduced dimension dataset achieved by applying principal component analysis (PCA) on the normalized data. The proposed procedure is evaluated through some well‐known and standard criteria such as error relative deviation, root mean square error, the percentage of the average absolute relative deviation %AARD, sum of squared error, standard deviation, and correlation factor (R2). Simulation and analytical results demonstrate that the designed PCA‐RBF‐NN procedure can precisely model the dynamics of energy consumption and the final water content in a typical GPP with the confidence level of 98.6% through six principal components achieved by PCA technique. Furthermore, small values of the error measurements are obtained while using the developed RBF‐NN model.

ACS Style

Ladan Khoshnevisan; Farzad Hourfar; Falah Alhameli; Ali Elkamel. Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant. International Journal of Energy Research 2020, 45, 5974 -5987.

AMA Style

Ladan Khoshnevisan, Farzad Hourfar, Falah Alhameli, Ali Elkamel. Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant. International Journal of Energy Research. 2020; 45 (4):5974-5987.

Chicago/Turabian Style

Ladan Khoshnevisan; Farzad Hourfar; Falah Alhameli; Ali Elkamel. 2020. "Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant." International Journal of Energy Research 45, no. 4: 5974-5987.

Journal article
Published: 16 November 2020 in Computers & Chemical Engineering
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In this paper, the effect of β-amyloid (Aβ) oligomer aggregates on Acetylcholine (ACh) neurocycle of human brain and its relation to Alzheimer's disease (AD) are investigated through a two-enzyme/two-compartment (2E2C) diffusion mathematical model whereby compartment 1 represents the presynaptic neuron while compartment 2 represents the postsynaptic neuron and postsynaptic cleft. It is found that Aβ aggregates induce choline leakage from the presynaptic neurons (compartment 1) leading to a reduction in choline content in both compartments. The lowering in choline levels in each compartment is promoted with the increase in the inlet Aβ oligomers which create more channels and pores in the presynaptic membrane. In addition, it is found that both the rates of ACh synthesis and hydrolysis were affected negatively by the increase in the levels of Aβ oligomers. Furthermore, the levels of ACh in both compartments decreased while the level of Aβ oligomers increased. However, the acetate concentration in compartment 1 increased but the acetate level in compartment 2 decreased. These results are compatible with the low levels of both ACh and choline in the neuron tissues of Alzheimer's disease (AD) brains through choline leakage hypothesis. Also, the model shows the significant effect of inhibiting Aβ aggregation as a promising therapeutic mechanism to mitigate cholinergic diseases. The model suggests that Aβ oligomers exert neuroinflammatory and toxic effect against the ACh neurocycle. The paper suggests that development of therapeutic agents capable of inhibiting toxic Aβ aggregations and maintaining choline substrates in neurons is necessary to provide a neuroprotective effect, cholinergic transmission, and maintain reasonable levels of ACh.

ACS Style

Ibrahim Mustafa; Asmaa Awad; Hedia Fgaier; Abdalla Mansur; Ali Elkamel. Compartmental modeling and analysis of the effect of β-amyloid on acetylcholine neurocycle via choline leakage hypothesis. Computers & Chemical Engineering 2020, 145, 107165 .

AMA Style

Ibrahim Mustafa, Asmaa Awad, Hedia Fgaier, Abdalla Mansur, Ali Elkamel. Compartmental modeling and analysis of the effect of β-amyloid on acetylcholine neurocycle via choline leakage hypothesis. Computers & Chemical Engineering. 2020; 145 ():107165.

Chicago/Turabian Style

Ibrahim Mustafa; Asmaa Awad; Hedia Fgaier; Abdalla Mansur; Ali Elkamel. 2020. "Compartmental modeling and analysis of the effect of β-amyloid on acetylcholine neurocycle via choline leakage hypothesis." Computers & Chemical Engineering 145, no. : 107165.

Journal article
Published: 17 October 2020 in Energy
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Reducing CO2 emissions from fossil fuel fired power plants has been a major environmental concern over the last decade. Amongst various carbon capture and storage (CCS) technologies, the utilization of solvent-based post-combustion capture (PCC), played a major role in the reduction of CO2 emissions. This paper illustrates the development of machine learning models to predict the outputs of the PCC unit. A fine tree, Matérn Gaussian process regression (GPR), rational quadratic GPR, and squared exponential GPR models were developed and compared with a feed-forward artificial neural network (ANN) model. An accuracy of up to 98% in predicting the process outputs was achieved. Furthermore, the models were utilized to determine the optimum operating conditions for the process using a sequential quadratic programming algorithm (SQP) and genetic algorithm (GA). The use of the machine learning models has proven to be very useful since the complete mechanistic model is too large, and its runtime is too long to allow for rigorous optimal solutions. The machine learning models and optimization problems were developed and solved using MATLAB. The data used in this work was obtained from simulating the process using gPROMS process builder. The inputs of the model were selected to be reboiler duty, condenser duty, reboiler pressure, flow rate, temperature, and the pressure of the flue gas. The models were able to accurately predict the outputs of the process which are the system energy requirements (SER), capture rate (CR), and the purity of condenser outlet stream (PU).

ACS Style

Abdelhamid Shalaby; Ali Elkamel; Peter L. Douglas; Qinqin Zhu; Qipeng P. Zheng. A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit. Energy 2020, 215, 119113 .

AMA Style

Abdelhamid Shalaby, Ali Elkamel, Peter L. Douglas, Qinqin Zhu, Qipeng P. Zheng. A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit. Energy. 2020; 215 ():119113.

Chicago/Turabian Style

Abdelhamid Shalaby; Ali Elkamel; Peter L. Douglas; Qinqin Zhu; Qipeng P. Zheng. 2020. "A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit." Energy 215, no. : 119113.

Journal article
Published: 03 September 2020 in Energies
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: Cheap and clean energy demand is continuously increasing due to economic growth and industrialization. The energy sectors of several countries still employ fossil fuels for power production and there is a concern of associated emissions of greenhouse gases (GHG). On the other hand, environmental regulations are becoming more stringent, and resultant emissions need to be mitigated. Therefore, optimal energy policies considering economic resources and environmentally friendly pathways for electricity generation are essential. The objective of this paper is to develop a comprehensive model to optimize the power sector. For this purpose, a multi-period mixed integer programming (MPMIP) model was developed in a General Algebraic Modeling System (GAMS) to minimize the cost of electricity and reduce carbon dioxide (CO2) emissions. Various CO2 mitigation strategies such as fuel balancing and carbon capture and sequestration (CCS) were employed. The model was tested on a case study from Pakistan for a period of 13 years from 2018 to 2030. All types of power plants were considered that are available and to be installed from 2018 to 2030. Moreover, capacity expansion was also considered where needed. Fuel balancing was found to be the most suitable and promising option for CO2 mitigation as up to 40% CO2 mitigation can be achieved by the year 2030 starting from 4% in 2018 for all scenarios without increase in the cost of electricity (COE). CO2 mitigation higher than 40% by the year 2030 can also be realized but the number of new proposed power plants was much higher beyond this target, which resulted in increased COE. Implementation of carbon capture and sequestration (CCS) on new power plants also reduced the CO2 emissions considerably with an increase in COE of up to 15%.

ACS Style

Adeel Arif; Muhammad Rizwan; Ali Elkamel; Luqman Hakeem; Muhammad Zaman. Optimal Selection of Integrated Electricity Generation Systems for the Power Sector with Low Greenhouse Gas (GHG) Emissions. Energies 2020, 13, 4571 .

AMA Style

Adeel Arif, Muhammad Rizwan, Ali Elkamel, Luqman Hakeem, Muhammad Zaman. Optimal Selection of Integrated Electricity Generation Systems for the Power Sector with Low Greenhouse Gas (GHG) Emissions. Energies. 2020; 13 (17):4571.

Chicago/Turabian Style

Adeel Arif; Muhammad Rizwan; Ali Elkamel; Luqman Hakeem; Muhammad Zaman. 2020. "Optimal Selection of Integrated Electricity Generation Systems for the Power Sector with Low Greenhouse Gas (GHG) Emissions." Energies 13, no. 17: 4571.

Journal article
Published: 17 August 2020 in Computers & Chemical Engineering
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Waterflooding is one of the most popular techniques which are generally used to increase oil recovery factor in mature reservoirs. A challenging issue in conducting the waterflooding process is how to handle the effects of exiting reservoir uncertainties. To this aim, in this paper an optimization algorithm based on Mixed H∞/passivity controller design is introduced. The presented approach is capable to systematically take into account the unpredicted influences of inherent geological uncertainties on the production regime, while guaranteeing the stability and disturbance attenuation in the closed-loop system. In addition, this technique expresses energy transition between system states and disturbances, which are representatives of the uncertainty effects. In this study, the optimization problem has been formulated such that the gained profit (here, the net present value: npv) is maximized, while dealing with the operational constraints and also the uncertainty impacts. The defined performance index is able to simultaneously achieve the H∞ performance and the passivity property, in the presence of inherent uncertainties. The optimization problem has been solved by Linear Matrix Inequality (LMI) approach. The developed algorithm has been simulated on 10th SPE-model#2 as a well-known case study, by generating hypothetical uncertainty in the permeability grids. The obtained results have shown that the designed controller can appropriately adjust the water injection profile, known as the manipulated variable, to achieve the maximum feasible npv in the presence of uncertainty and operational constraints. Finally, further analysis has been provided to compare the introduced methodology with conventional robust optimization approach.

ACS Style

Farzad Hourfar; Ladan Khoshnevisan; Behzad Moshiri; Karim Salahshoor; Ali Elkamel. Mixed H/Passivity controller design through LMI approach applicable for waterflooding optimization in the presence of geological uncertainty. Computers & Chemical Engineering 2020, 142, 107055 .

AMA Style

Farzad Hourfar, Ladan Khoshnevisan, Behzad Moshiri, Karim Salahshoor, Ali Elkamel. Mixed H/Passivity controller design through LMI approach applicable for waterflooding optimization in the presence of geological uncertainty. Computers & Chemical Engineering. 2020; 142 ():107055.

Chicago/Turabian Style

Farzad Hourfar; Ladan Khoshnevisan; Behzad Moshiri; Karim Salahshoor; Ali Elkamel. 2020. "Mixed H/Passivity controller design through LMI approach applicable for waterflooding optimization in the presence of geological uncertainty." Computers & Chemical Engineering 142, no. : 107055.

Journal article
Published: 15 August 2020 in Sustainability
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Eco-industrial parks (EIPs) are promoting a shift from the traditional linear model to the circular model, where industrial symbiosis plays an important role in encouraging the exchange of materials, energy, and waste. This paper proposes a generalized framework to design eco-industrial parks, and illustrates it with regard to the end-of-life vehicle problem (ELV). An eco-industrial park for end-of-life vehicles (EIP-4-ELVs) creates synergy in the network that leverages waste reduction and efficiently uses resources. The performance of the proposed framework is investigated along with the interactions between nodes. The proposed framework consists of five steps: (1) finding motivation for EIP, (2) identifying all entities with industrial symbiosis, (3) pinpointing the anchor entity, (4) determining industrial symbiosis between at least three entities and two exchange flows, and (5) defining exchange-flow types. The two last steps are connected by a feedback loop to allow future exchange flows. The proposed framework serves as a guideline for decision makers during the first stages of developing EIPs. Furthermore, the framework can be linked to car-design software to predict the recyclability of vehicle components and aid in manufacturing vehicles optimized for recycling.

ACS Style

Shimaa Al-Quradaghi; Qipeng Zheng; Ali Elkamel. Generalized Framework for the Design of Eco-Industrial Parks: Case Study of End-of-Life Vehicles. Sustainability 2020, 12, 6612 .

AMA Style

Shimaa Al-Quradaghi, Qipeng Zheng, Ali Elkamel. Generalized Framework for the Design of Eco-Industrial Parks: Case Study of End-of-Life Vehicles. Sustainability. 2020; 12 (16):6612.

Chicago/Turabian Style

Shimaa Al-Quradaghi; Qipeng Zheng; Ali Elkamel. 2020. "Generalized Framework for the Design of Eco-Industrial Parks: Case Study of End-of-Life Vehicles." Sustainability 12, no. 16: 6612.

Journal article
Published: 27 July 2020 in International Journal of Hydrogen Energy
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This paper applies a mixed integer linear programming model developed in GAMS to simulate the integration of Power-to-Gas infrastructure into an industrial manufacturer's energy system subject to the existing thermal and electrical energy demands, as well as a third hydrogen energy profile. This work is novel in that it assesses the challenges and economic incentives available to make feasible the installation of a hydrogen-based energy storage systems within the Province of Ontario from a techno-economic, policy and environmental perspectives. The energy hub analyzed in this work uses electricity from the power grid and solar PVs to meet the manufacturer's demands, while converting the excess to hydrogen gas, which is used across an array of pathways to generate revenue. ThisThis includes a blend ofof hydrogen for fuel cell vehicles (FCVs), hydrogen for forklifts, and the direct injection of hydrogen into the facility's natural gas, adding renewable content to the heating, and manufacturing processes. Our primary objective was to implement a safe design that minimizes capital and operating costs, resulting in a favorable business case for producing hydrogen, and providing ancillary grid services. However, Power-to-Gas creates a net-emission reduction that can be used not only to sell emission allowances in the provincial carboncarbon tax program for up to $30/t-CO2eq but to assist the Company in achieving their strategic emission reduction targets. Installation of the selected Power-to-Gas system would require a total capital investment of $2,620,448 with the electrolyzers and solar panels accounting for 41% and 17% of the capital costs, respectively. The compressors will account for most of the operating costs which total $237,653 annually. Within the energy hub, 76,073 kg-H2 has been produced per year for end-use applications. A sensitivity analysis was performed by varying both hydrogen and carbon credit price which predicted a potentialpotential CO2 offset of 2359.7 tonne/yr with a payback period of as little as 2.8 years.

ACS Style

Nicholas Preston; Azadeh Maroufmashat; Hassan Riaz; Sami Barbouti; Ushnik Mukherjee; Peter Tang; Javan Wang; Ehsan Haghi; Ali Elkamel; Michael Fowler. How can the integration of renewable energy and power-to-gas benefit industrial facilities? From techno-economic, policy, and environmental assessment. International Journal of Hydrogen Energy 2020, 45, 26559 -26573.

AMA Style

Nicholas Preston, Azadeh Maroufmashat, Hassan Riaz, Sami Barbouti, Ushnik Mukherjee, Peter Tang, Javan Wang, Ehsan Haghi, Ali Elkamel, Michael Fowler. How can the integration of renewable energy and power-to-gas benefit industrial facilities? From techno-economic, policy, and environmental assessment. International Journal of Hydrogen Energy. 2020; 45 (51):26559-26573.

Chicago/Turabian Style

Nicholas Preston; Azadeh Maroufmashat; Hassan Riaz; Sami Barbouti; Ushnik Mukherjee; Peter Tang; Javan Wang; Ehsan Haghi; Ali Elkamel; Michael Fowler. 2020. "How can the integration of renewable energy and power-to-gas benefit industrial facilities? From techno-economic, policy, and environmental assessment." International Journal of Hydrogen Energy 45, no. 51: 26559-26573.

Journal article
Published: 16 July 2020 in Electronics
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Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.

ACS Style

Amin Mansour-Saatloo; Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Ali Ahmadian; Ali Elkamel. Machine Learning Based PEVs Load Extraction and Analysis. Electronics 2020, 9, 1150 .

AMA Style

Amin Mansour-Saatloo, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, Ali Ahmadian, Ali Elkamel. Machine Learning Based PEVs Load Extraction and Analysis. Electronics. 2020; 9 (7):1150.

Chicago/Turabian Style

Amin Mansour-Saatloo; Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Ali Ahmadian; Ali Elkamel. 2020. "Machine Learning Based PEVs Load Extraction and Analysis." Electronics 9, no. 7: 1150.

Journal article
Published: 09 July 2020 in Energy Conversion and Management
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Biomass is deemed to be an important contributor to satisfy our energy, chemicals and material requirements throughout the world. The present study aimed to study the bioenergy potential of Staghorn Sumac (SS) through modified distributed activation energy model (DAEM), kinetic models, thermogravimetric analyzer, elemental analyzer, Fourier transform infrared spectrometry (FTIR) and gas chromatography-mass spectrometry (GC–MS). Pyrolysis experiments were carried out at the different heating rates of 10, 20, 30 and 40 °C min−1 to study kinetics. The average activation energy values achieved through DAEM, KAS, FWO and Starink models were 160, 167, 169, and 168 kJ mol−1, respectively. Additionally, an Artificial Neural Network (ANN) model was equated with modified DAEM. Moreover, The composition of evolved gas compound measured by a gas chromatography coupled with mass spectroscopy showed that bio-oil mainly consisted of 82.33% acid, 6.37% aldehyde and ketone, 4.96% amid, 2.76% ester, 2.07% aromatic and alcohols, and 1.52% other groups. This study has revealed the remarkable potentials of Staghorn Sumac for clean bioenergy production.

ACS Style

Muhammad Sajjad Ahmad; Hui Liu; Hesham Alhumade; Muddasar Hussain Tahir; Gülce Çakman; Ağah Yıldız; Selim Ceylan; Ali Elkamel; Boxiong Shen. A modified DAEM: To study the bioenergy potential of invasive Staghorn Sumac through pyrolysis, ANN, TGA, kinetic modeling, FTIR and GC–MS analysis. Energy Conversion and Management 2020, 221, 113173 .

AMA Style

Muhammad Sajjad Ahmad, Hui Liu, Hesham Alhumade, Muddasar Hussain Tahir, Gülce Çakman, Ağah Yıldız, Selim Ceylan, Ali Elkamel, Boxiong Shen. A modified DAEM: To study the bioenergy potential of invasive Staghorn Sumac through pyrolysis, ANN, TGA, kinetic modeling, FTIR and GC–MS analysis. Energy Conversion and Management. 2020; 221 ():113173.

Chicago/Turabian Style

Muhammad Sajjad Ahmad; Hui Liu; Hesham Alhumade; Muddasar Hussain Tahir; Gülce Çakman; Ağah Yıldız; Selim Ceylan; Ali Elkamel; Boxiong Shen. 2020. "A modified DAEM: To study the bioenergy potential of invasive Staghorn Sumac through pyrolysis, ANN, TGA, kinetic modeling, FTIR and GC–MS analysis." Energy Conversion and Management 221, no. : 113173.

Journal article
Published: 17 June 2020 in Energies
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Power-to-gas is an energy storage and vector technology which can utilize off-peak power, assist in the integration of renewable power and provide needed fuel for industry and transportation. Further, power-to-gas is a useful technology for balancing surplus baseload and renewable energy generation with demand. There are numerous applications of power-to-gas in Europe, where renewable power is used to generate hydrogen for numerous applications. Examining each of these power-to-gas pathways across quantitative and qualitative criteria, this paper utilizes the stochastic fuzzy analytic hierarchy process to determine criteria weights. These weights are then fed to a multiple criteria decision analysis tool to determine the viability of each pathway for investors and policy makers. A sensitivity analysis is carried out by reprioritizing the criteria and re-evaluating the multiple criteria analysis. The two pathways that score highest under multiple criteria rankings are power-to-gas to mobility-fuel and power-to-gas-to-power, due to their established technologies, lower costs and environmental performance. By extension, both of these power-to-gas pathways are the most appropriate ways for this technology to be implemented, due to their combination of public familiarity, emissions reductions, and developed, available technologies.

ACS Style

Sean Walker; Suadd Al-Zakwani; Azadeh Maroufmashat; Michael Fowler; Ali Elkamel. Multi-Criteria Examination of Power-to-Gas Pathways under Stochastic Preferences. Energies 2020, 13, 3151 .

AMA Style

Sean Walker, Suadd Al-Zakwani, Azadeh Maroufmashat, Michael Fowler, Ali Elkamel. Multi-Criteria Examination of Power-to-Gas Pathways under Stochastic Preferences. Energies. 2020; 13 (12):3151.

Chicago/Turabian Style

Sean Walker; Suadd Al-Zakwani; Azadeh Maroufmashat; Michael Fowler; Ali Elkamel. 2020. "Multi-Criteria Examination of Power-to-Gas Pathways under Stochastic Preferences." Energies 13, no. 12: 3151.

Article
Published: 23 April 2020 in The Canadian Journal of Chemical Engineering
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This paper describes the development of “heats” and input variables selection models that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The selection models in this work are developed based on latent variable methods. The latent variable methods used in this work are multiway principal component analysis (MPCA) and multiway projection to latent structures (MPLS). The particular problems related to latent variable methods discussed in this paper include data preprocessing, including alignment, unfolding method, centring, and scaling. The outcome of the heats selection model is heats with normal operation and the outcome of the input variables selection model is variables that are highly correlated with the off‐gas water vapor. The water detection framework may be implemented on an industrial AC EAF, based on the obtained selection models.

ACS Style

Hamzah Alshawarghi; Ali Elkamel; Behzad Moshiri; Farzad Hourfar. Heats and input variables selection for designing a water detection framework applicable to industrial electric arc furnaces. The Canadian Journal of Chemical Engineering 2020, 98, 2096 -2108.

AMA Style

Hamzah Alshawarghi, Ali Elkamel, Behzad Moshiri, Farzad Hourfar. Heats and input variables selection for designing a water detection framework applicable to industrial electric arc furnaces. The Canadian Journal of Chemical Engineering. 2020; 98 (10):2096-2108.

Chicago/Turabian Style

Hamzah Alshawarghi; Ali Elkamel; Behzad Moshiri; Farzad Hourfar. 2020. "Heats and input variables selection for designing a water detection framework applicable to industrial electric arc furnaces." The Canadian Journal of Chemical Engineering 98, no. 10: 2096-2108.