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In the past decade, there has been a steady increase in the focus on green initiatives for data centers. Various energy efficiency measures have been proposed and adopted, however the optimal tradeoff between performance and energy efficiency of data centers is yet to be achieved. Addressing this issue, we present APEnergy, an Application Profile-based energy efficient framework for small to medium scale data centers. The proposed framework leverages information on the completed application with certain workloads in the data center to build profiles for workflows. The framework utilizes a novel scheduler to obtain a near-optimal mapping for placement of workflow tasks in the data center based on three criteria including CPU utilization, power cost and task completion time. We compare the performance of the proposed scheduler to similar RTC and HEFT schedulers. Extensive simulation studies are carried out to verify the scalability and efficiency of APEnergy framework. Results show that the proposed Scheduler is 2% and 14% more energy efficient than RTC and HEFT respectively.

In the present work, a novel hybrid solar-based smart building energy system is introduced and studied. The system comprises innovative photovoltaic-thermal-cooling (PVTC) panels integrated with hot and cold storages with two-way interaction with electricity, heat, and cooling networks (if any). The proposed system is compared with PV-based systems integrated with battery and heat pump for a case study complex building in Aarhus, Denmark. The comparison is conducted by evaluating the performance and economic indicators and investigating the effect of significant parameters on each scenario via a parametric study. Furthermore, the optimal operating conditions and sizing of the proposed system are determined using the genetic algorithm method considering initial cost and traded energy with local energy networks as the objective functions. The comparison results show that the proposed solution is the most cost-effective scenario with the lowest initial cost of about 457,000 $ and a payback period of 6.6 years. This is mainly due to the simultaneous interaction with electricity/heat/cooling networks as well as the elimination of the battery and the heat pump, which are offered by the proposed scenario. It is shown that, in comparison to PV panels, the PVTC can produce 328.7 MWh and 125.6 MWh extra heat and cooling annually. The scatter distribution of significant parameters shows that the panel area and heat storage capacity are not sensitive parameters, and keeping the cold storage capacity at the lower bound is a techno-economically better option.

The closed-loop supply chain management (CLSCM) is an attractive research field for the corporate and academic worlds; however, closing the loop is not a simple task. Reverse logistics activities increase management complexities and uncertainties by establishing multi-fold collection and return management processes. Unlike traditional supply chain management, where managers deal with only stochastic demand, in closed-loop supply chain management, they deal with both stochastic demand and returns, which increases the cumulative uncertainty in the system. Firms usually use disposable packaging, and demand uncertainties also increase the negative environmental implications of logistics activities. This study aims to investigate optimal remanufacturing strategy and reusable packaging capacity under stochastic demand and return rate for single and multi-retailer closed-loop supply chain models. The results show that a hybrid policy is an optimal option for both single and multi-retailer cases; however, the rate of remanufacturing increases for multiple-retailers. Furthermore, remanufacturing cost, manufacturing cost, and ordering cost of retailers are the principal drivers of hybrid supply chain management. The results further suggest that supply chain managers should reduce manufacturing and remanufacturing costs because they play a central role in deciding the optimal remanufacturing rate. Increasing the remanufacturing rate increases ordering quantities and reduces setup and ordering costs in the system. Thus the remanufacturing is a relatively inexpensive policy for supply chains with higher setup and ordering costs. Numerical examples, sensitivity analysis, and comparative study show the robustness and validity of the proposed model.

PurposeThe purpose of this paper is to explore the trajectories of the urbanization process in Saudi Arabia in its regional context from the unification of the country by King Abdul Aziz Al Saud in 1932 to the present time, and the urbanization impact on the status and management of cultural heritage in the Kingdom.Design/methodology/approachOur study design integrated a well-articulated theoretical frame of sustainability to gain a heuristical understanding of urbanization in Saudi Arabia, and its link to cultural heritage. The methodological approach was mixed in nature involving (1) literature search and review, (2) analysis of public documents and databases, (3) analysis of photographs and (4) expert interviews.FindingsOne of the most obvious findings reached in this study is that there is considerable trade-off between heritage site conservation, population and economic demand for increased urbanization. Hence, with increasing urbanization pressures, the value of the heritage site may be rethought based on Saudi Arabia's economic and cultural conservation perspectives.Research limitations/implicationsSince our data are mostly of textual narrative in origin, precise predictions were difficult or impossible for many reasons such as non-linearity, and non-equilibrium dynamics, context and scale dependence as well as the historical exigency of urbanization. However, the same theoretical framework can be applied to appropriate longitudinal/ time series data for predictive analyses, which can be taken up as a future research agenda.Originality/valueThis paper analyzes the urbanization process and sustainability challenges of cultural heritage sites employing a mixed methodological approach, embedded in a holistic theoretical framework of sustainability.

As a clean and renewable energy, bioenergy is one of the most prospective alternatives to fossil fuels. Therefore, it has been received much attention and occupied a considerable status in the world's energy consumption. Moreover, the sustainable bioenergy production can effectively decrease the risk of energy poverty and contribute to the economic and ecological benefits, particularly in developing countries. However, the selection of bioenergy production technologies (BPTs) has significant challenges and uncertainties due to various factors. Thus, the aim of this study is to evaluate the BPTs and choose the most suitable and sustainable alternative among them. For this purpose, an integrated decision-making framework is proposed under single-valued neutrosophic environment. In the present approach, the method based on the removal effects of criteria (MEREC) and stepwise weight assessment ratio analysis (SWARA) weighting integrated procedure (MASWIP) is introduced to compute the relative significances of the considered sustainability indicators. Then, the MASWIP-based complex proportional assessment (COPRAS) is introduced to prioritize the BPTs from single-valued neutrosophic perspective. Further, the present framework is implemented on a case study of BPTs evaluation problem with uncertain, inconsistent and indeterminate information, which approves the effectiveness and practicality of introduced method. To assess its robustness and stability, sensitivity and comparative studies are performed. The advantages of the developed method are emphasized in terms of stability and reliability by comparisons with five existing approaches, and the effectiveness of the present approach is verified. The findings of the study prove that the application of single-valued neutrosophic MASWIP-based COPRAS method in BPT selection is robust.

The heating and Cooling loads are the main contributors to energy consumption in buildings, and predicting them can prevent many potential financial losses in civil engineering projects. Using the benefits of the neural networks, including support vector machine, gated recurrent unit, extreme learning machine, long short-term memory, and shuffled frog leaping algorithm as an optimizer, the present study aims to predict the energy consumption of the building. The empirical data are trained using the selected networks and optimized through a shuffled frog-leaping algorithm. Also, the statistical criteria are analyzed to specify the best network in terms of accuracy and speed. The obtained results and the convergence rate represent the remarkable capability of the shuffled frog leaping algorithm for optimization. According to the statistical results, long short-term memory and support vector machine are introduced as the best neural network for cooling and heating load forecast, respectively. According to the obtained results, for the cooling load prediction, LSTM-SFLA presents the best performance by an R2 of 0.9761. On the other hand, for the heating load prediction, SVR-SFLA has the optimal performance with an R2 of 0.9583. The results indicate that using the SFLA optimizer could assist in improving the prediction performance.

This study aims to investigate the thermo-kinetics of high-ash sewage sludge using thermogravimetric analysis. Sewage sludge was dried, pulverized and heated non-isothermally from 25 to 800 °C at different heating rates (5, 10 and 20 °C/min) in N2 atmosphere. TG and DTG results indicate that the sewage sludge pyrolysis may be divided into three stages. Coats-Redfern integral method was applied in the 2nd and 3rd stage to estimate the activation energy and pre-exponential factor from mass loss data using five major reaction mechanisms. The low-temperature stable components (LTSC) of the sewage sludge degraded in the temperature regime of 250–450 °C while high-temperature stable components (HTSC) decomposed in the temperature range of 450–700 °C. According to the results, first-order reaction model (F1) showed higher Ea with better R2 for all heating rates. D3, N1, and S1 produced higher Ea at higher heating rates for LTSC pyrolysis and lower Ea with the increase of heating rates for HTSC pyrolysis. All models showed positive ΔH except F1.5. Among all models, Diffusion (D1, D2, D3) and phase interfacial models (S1, S2) showed higher ΔG as compared to reaction, nucleation, and power-law models in section I and section II.

Guaranteeing long-term sustainability and developing environmentally friendly systems can be addressed with multi-generation systems that use renewable energy sources. Accordingly, a parabolic trough solar collector field was used to drive an organic Rankine cycle-ejector refrigeration integration, a modified organic Rankine cycle by regenerator, and a proton exchange membrane electrolyzer to produce power, cooling, and hydrogen. The energy, exergy, and exergoeconomic approaches were applied to assess the designed system's feasibility. The Pentane-Butane zeotropic mixture was utilized to enhance the performance of the organic Rankine cycle-ejector refrigeration integration. Finally, multi-objective optimization is performed to reach the system's optimum operating conditions. The obtained results at the base conditions revealed that the designed plant could yield 1501 kW total net power, where 192.9 kW was produced by the modified organic Rankine cycle. Also, the cooling load and hydrogen production were obtained at about 433 kW and 3.71 kg/h. Moreover, the system reached 6.68% exergetic efficiency with 5.17 years payback period at the optimum state. According to the obtained results, the proposed plant can be feasible for construction that could help sustainable development plans.