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Wastewater treatment plants (WWTPs) offer opportunities to optimize resource utilization and enhance energy efficiency. This study provides a comprehensive analysis of using the polygeneration approach in WWTPs to reduce grid energy dependence, optimize energy distribution, and utilize surplus energy for hydrogen (H2) and ammonia (NH3) production. Several models were employed, including photovoltaic (PV) cells, parabolic trough collectors (PTCs), steam methane reforming, and polymer electrolyte membranes, to assess the feasibility of this approach. Three scenarios were evaluated and compared: Scenario 1 (Baseline) represents the current situation, Scenario 2 maximizes the Net Present Value (NPV), and Scenario 3 minimizes NH3production costs. Real data from As-Samra WWTP in Jordan was used to accurately assess the feasibility of each scenario. The results show that Scenario 2 offers the highest profitability and efficiency, with a NPV of 87.48 million USD and an annual NH3production of 15,417 tons, reducing both grid dependency and biogas fuel consumption. Both Scenarios 2 and 3 demonstrate the ability to meet thermal demands efficiently while generating significant revenue from NH3production. Scenario 3, in particular, achieves competitive H2and NH3production costs. Environmentally, Scenario 2 significantly reduces annual greenhouse gas emissions by 12.66 kilotons of CO2eq, with near-zero carbon intensity for thermal energy due to solar reliance. In conclusion, the polygeneration approach offers a promising pathway for WWTPs to achieve greater sustainability, economic gains, and reduced environmental impact, providing valuable insights for decision-makers.

A critical aspect for prognostics and health management is the prediction of the remaining useful life (RUL). The existing RUL prediction techniques for aluminum electrolytic capacitors mostly assume the operating conditions remain constant for the entire prediction timeline. In practice, the electrolytic capacitors experience large variations in operating conditions during their lifetime that influence their degradation process and RUL. This paper proposes a RUL prediction method based on deep learning. The proposed framework uses the original condition monitoring and operating condition data without the necessity of assuming any particular type of degradation process and, therefore, avoiding the requirement of establishing link between model parameters and operating conditions. The proposed framework first identifies the degrading point and then develops the Long Short-Term Memory (LSTM) model to predict the RUL of capacitors. The LSTM-based method can reduce the computational time and complexity while ensuring high prediction performance. Its effectiveness is demonstrated by utilizing the simulated degradation process and temperature condition time-series of aluminum electrolytic capacitors used in electric vehicle powertrain.

Wastewater treatment plants play a vital role in resource recovery, particularly through biogas production, a key renewable energy source. Beyond biogas, the digestate from anaerobic digestion is rich in nutrients like ammonia. This study explored the feasibility of recovering ammonia from sewage sludge digestate using air stripping. The process was modeled using Aspen Plus® software, utilizing real data from As-Samra WWTP in Jordan. Various operational parameters, such as digestate feed flow, air flow rate, temperature, and pressure, were analyzed to optimize ammonia recovery. The results showed that with a feed flow rate between 10,000 and 30,000 kg/hr, ammonia recovery reached 85%, with production exceeding 100 kg/hr, where the effect of the flow rate appears mostly at elevated feeding temperatures. Increased air flow rates significantly boosted recovery, achieving 90% efficiency at 60 °C with 50,000 kg/h as air flow. Flashing pressure peaked at 1.5 bar, with 85% efficiency at 95 °C, while higher pressures yielded diminishing returns, stabilizing production around 106 kg/hr. The NaOH feed rate also influenced output, rising from 100 kg/hr at a 50 kg/hr feed rate to 107 kg/hr at 750 kg/hr, with recovery efficiency exceeding 85%. The economic analysis showed that the project had a payback period of 6.07 years, reflecting a reasonable recovery of the initial investment. The net present value was 122,924 USD over 15 years, with 8% amortization rate, indicating that the project created value beyond the initial cost. The internal rate of return was 14.23%, surpassing the discount rate and highlighting the project's financial attractiveness.

•Waste heat from WWTPs used for desalination modeled with TRNSYS® and ASPEN PLUS®.•MED process evaluated in series and parallel configurations under varied conditions.•Optimization combines machine learning with Pelican Optimization Algorithm (POA).•Series MED yields 3984.2 kg/hr productivity, 3.78 performance ratio, 0.991 recovery ratio.•Parallel MED yields 4421 kg/hr productivity, 3.81 performance ratio, 0.851 recovery ratio.

We present a novel method for concept drift detection, based on: 1) the development and continuous updating of online sequential extreme learning machines (OS-ELMs) and 2) the quantification of how much the updated models are modified by the newly collected data. The proposed method is verified on two synthetic case studies regarding different types of concept drift and is applied to two public real-world data sets and a real problem of predicting energy production from a wind plant. The results show the superiority of the proposed method with respect to alternative state-of-the-art concept drift detection methods. Furthermore, updating the prediction model when the concept drift has been detected is shown to allow improving the overall accuracy of the energy prediction model and, at the same time, minimizing the number of model updatings.

This study aims to replace the current conventional chilled-water system with solar-assisted absorption cooling in a large pharmaceutical company in Amman, Jordan. Conventional heating, ventilation, and air conditioning (HVAC) systems were used to serve the existing 9000 m2area of the pharmaceutical facilities. Two solar absorption chiller configurations were proposed and evaluated to investigate this replacement using TRNSYS® software. The auxiliary heater and storage tank were configured in series and parallel and evaluated based on actual data from the company. The effects of the evacuated tube collector (ETC) on the solar fraction, storage tank volume (STV), auxiliary heat required (Qaux), and heat loss (Qloss) were studied. The grasshopper optimization algorithm (GOA), a newly developed optimization technique, was used to optimize the storage tank volume with solar fraction (SF), Qaux, and average heat loss (kJ/h) on an hourly basis in parallel and series configurations. Mathematical equations involving the factor such as storage tank volume and Qlossresponses in parallel and series configurations were developed using regression modeling (RM) over all experimental observations. The optimal factor values were then calculated using the GOA and regression models. The confirmation findings indicated that SF was maximized, and Qlosswas effectively minimized. Under the 95 % confidence level with = 0.05, the p-value for all RMs was <0.05, indicating the significance of the RM. Furthermore, the RM matched the provided data well, with a low prediction error. The GOA indicated that the parallel system configuration was the superior connection that provided optimum results for SF, and Qauxat a storage tank of 2.8 m3. According to the GOA method, the optimal STV for a series layout was 2.0 m3and for a parallel arrangement was 2.8 m3. In terms of SF, the parallel system configuration outperformed the series system, which increased by 12.34 %. The parallel system significantly decreases Qlossand Qauxby 4.04 % and 7.39 %, respectively. To compare the GOA with other meta-heuristic optimization approaches, conventional HVAC systems were optimized using particle swarm optimization (PSO). The results indicated that the GOA surpasses the PSO technique in terms of the required convergence speed and population size. Because the movement of agents depends on the positions of each agent inside the swarm, GOA differs from other optimization algorithms. The novelty of this study demonstrates that the GOA technique provides a higher probability of convergence with less computing effort than well-liked and popular optimization methodologies such as PSO for a variety of Qloss, Qaux, and SF issues. These results have significant reverberation for the potential of the GOA to minimize Qlossand Qaux, and maximize SF in the parallel and series configurations of the auxiliary heater.

In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity–based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity–based models.

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