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A large amount of solid and liquid waste is produced in pesticide production. It is necessary to adopt appropriate disposal processes to reduce pollutant emissions. A co-incineration scheme for mixing multi-component wastes in a rotary kiln was proposed for waste disposal from pesticide production. According to the daily output of solid and liquid wastes, the proportion of mixing was determined. An experiment of the co-incineration of solid and liquid wastes was established. Experimental results showed that the mixed waste could be completely disposed at 850 °C, and the residence time in the kiln exceeded 1 h. A model method for mixture and diesel oil-assisted combustion was proposed. Numerical simulation was performed to predict the granular motion and reveal the combustion interactions of the co-incineration of mixed wastes in the rotary kiln. Simulation results reproduced movements, such as rolling and cascading, and obtained the optimum rotational speed and diesel oil flow for the rotary kiln incineration operation. The simulation showed that the temperature in the kiln was maintained at 850 °C, and the mass fraction of CO and O2 at the outlet reached the standard for the complete combustion of the waste. Finally, the rotary kiln incineration and flue gas treatment processes were successfully applied in engineering for green production of pesticides.

This research paper evaluates hydrothermal carbonization (HTC) as a possible treatment for sewage sludge, including phosphorus recycling. German governmental requirements force a high number of wastewater treatment plants (WWTP) to recover phosphorus from sewage sludge above limits of 20 g kgTS−1 before further disposal (e.g., co-incineration). The results show that pH reduction has a positive effect on shifting phosphorus to the liquid phase during HTC. Although the experimental results of this research do not yet achieve the necessary phosphorus reduction, various calculations are made to achieve this goal in future experimental studies. In order to be able to assess the energy benefits of HTC, Aspen Plus modeling was used to show the positive impact of implementing this technology in a WWTP. It is shown that the mechanical dewaterability of sewage sludge (SS) increases after HTC enabling energy savings by means of subsequent thermal drying. A heat optimized HTC is able to cut energy expenses by half, further providing a phosphorus-depleted hydrochar for extensive energetic use.

Waste-printed circuit boards (WPCBs) account for approximately 3–6 wt% of total electronic waste. Due to their content of thermosetting materials and added brominated fire retardants, their recycling and disposal is difficult and not eco-friendly. Pyrolysis as a thermal degradation process may assist in the solution of this problem. In addition, using biomass as an additive can upgrade the bio-oil and fix bromines in the char. In this study, cotton stalk (CS) is chosen as an additive and kinetic of the pyrolysis of three samples namely: PCB, CS, and CS:PCB (50:50) were investigated by the thermogravimetric analyzer (TGA) at heating rates of 5, 10, and 15 K/min. Three non-isothermal methods: FWO, KAS, and Starink were found in good agreement with the TGA data; however, the FWO method was more efficient in the description of the degradation mechanism of solid-state reactions. For CS and CS:PCB (50:50), α was increased from 0.2 to 0.9 with the FWO method, and calculated Eα values were found in the range of 121.43–151.88 and 151.60–105.67 kJ/mol in zone 1, while 197.06–79.22 and 115.90–275.06 kJ/mol in zone 2, respectively. Whereas, for PCB in zone 1, Eα values were found to be in the range of 190.23–93.88 kJ/mol. The possible decomposition mechanism was determined by the Criado method, which was in agreement with the mechanism model for reaction order n = 3. The oil product was also analyzed using Fourier-Transform Infrared Spectroscopy analysis.

Cement hydration within particle bed concrete 3D printing processes can be benefited by storing water in the otherwise dry aggregate bulk material. Additional water also has the advantage of acting as a source of passive cooling. However, even small amounts of liquid lead to detrimental effects on bulk properties, such as the flowability. For that reason, this study proposes implementing dry water (DW) in order to store large amounts of water in a bulk material of non-absorbent, coarse sand whilst maintaining its initial bulk properties. DW is essentially created by mixing water and hydrophobic fumed silica in a high shear process, leading to water droplets surrounded by a protective silica shell. Herein, several DW variants, distinguished by their deionised water to hydrophobic silica ratio, were mixed with non-absorbent, coarse sand particles. In addition, mixtures were produced to contain a specific overall water content of up to wH2O = 5% within the bulk material. It was shown that dry water can be used to incorporate large amounts of water into a granular bulk material and simultaneously preserve flow properties. The decisive factor is the proportion of hydrophobic silica for a given water content as the DW capsules may otherwise not endure mechanical stress during mixing. However, even minimal quantities of silica can prevent liquid capillary bridges from forming and, thus, inhibit bulk property degradation.

In this paper, a novel method is proposed for the incorporation of data-driven machine learning techniques into process optimization. Such integration improves the computational time required for calculations during optimization and benefits the online application of advanced control algorithms. The proposed method is illustrated via the chemical absorption-based postcombustion CO2 capture process, which plays an important role in the reduction of CO2 emissions to address climate challenges. These processes simulated in a software environment are typically based on first-principle models and calculate physical properties from basic physical quantities such as mass and temperature. Employing first-principle models usually requires a long computation time, making process optimization and control challenging. To overcome this challenge, in this study, machine learning algorithms are used to simulate the postcombustion CO2 capture process. The extreme gradient boosting (XGBoost) and support vector regression (SVR) algorithms are employed to build models for prediction of carbon capture rate (CR) and specific reboiler duty (SRD). The R2 (a statistical measure that represents the fitness) of these models is, on average, greater than 90% for all the cases. XGBoost and SVR take 0.022 and 0.317 s, respectively, to predict CR and SRD of 1318 cases, whereas the first-principal process simulation model needs 3.15 s to calculate one case. The models built by XGBoost are employed in the optimization methods, such as an agent-based approach represented by the particle swarm optimization and stochastic technique indicated by the simulated annealing, to find specific optimal operating conditions. The most economical case, in which the CR is 72.2% and SRD is 4.3 MJ/kg, is obtained during optimization. The results show that computations with the data-driven models incorporated in the optimization technique are faster than first-principle modeling approaches. Thus, the application of machine learning techniques in the optimization of carbon capture technologies is demonstrated successfully.

Anomaly detection for core temperature has great significance in maintaining the safety of nuclear power plants. However, traditional auto-encoder-based anomaly detection methods might extract the latent space features with redundancy, which may lead to missing and false alarms. To address this problem, the idea of feature disentangling is introduced under the auto-encoder framework in this paper. First, a feature disentangling auto-encoder (DAE) is proposed where a latent space disentangling loss is designed to disentangle the features. We further propose an incrementally feature disentangling auto-encoder (IDAE), which is the improved version of DAE. In the IDAE model, an incremental feature generation strategy is developed, which enables the model to evaluate the disentangling degree to adaptively determine the feature dimension. Furthermore, an iterative training framework is designed, which focuses on the parameter training of the newly incremented feature, overcoming the difficulty of model training. Finally, we illustrate the effectiveness and superiority of the proposed method on a real nuclear reactor core temperature dataset. IDAE achieves average false alarm rates of 4.745% and 6.315%, respectively, using two monitoring statistics, and achieves average missing alarm rates of 6.4% and 2.9%, respectively, using two monitoring statistics, outperforming the other methods.

In order to quantitatively evaluate and improve the sustainability of machining systems, this paper presents an emergy (the amount of energy consumed in direct and indirect transformations to make a product or service) based sustainability evaluation and improvement method for machining systems, contributing to the improvement of energy efficiency, resource efficiency and environmental performance, and realizing the sustainability development. First, the driver and challenge are studied, and the scope and hypothesis of the sustainable machining system are illustrated. Then, the emergy-based conversion efficiency model is proposed, which are (1) effective emergy utilization rate (EEUR), (2) emergy efficiency of unit product (EEUP) and (3) emergy conversion efficiency (ECE), to measure and evaluate the sustainable machining system from the perspectives of energy, resource and environment. Finally, the proposed model is applied to a vehicle-bridge machining process, and the results show that this paper provides the theoretical and method support for evaluating and improving the sustainable machining processes to decouple the resources and development of the manufacturing industry.

The need for public fast electric vehicle charging station (FEVCS) infrastructure is growing to meet the zero-emission goals of the transportation sector. However, the large charging demand of the EV fleet may adversely impact the grid’s stability and reliability. To improve grid stability and reliability, the development of a DC microgrid (MG) leveraging renewable energy sources to supply the energy demands of FEVCSs is the sustainable solution. Balancing the intermittent EV charging demand and fluctuating renewable energy generation with the stable DC bus voltage of a DC MG is a challenging objective. To address this objective, a piece-wise droop control strategy is proposed in this work. The proposed scheme regulates DC bus voltage and power sharing with droop value updating in a region-based load current distribution. Voltage compensation in individual regions is carried out to further improve the degree of freedom. In this paper, the performance of the proposed strategy is evaluated with the consideration of real-time solar PV dynamics and EV load dynamics. Further, to showcase the effectiveness of the proposed strategy, a comparative analysis with a maximum power point tracking (MPPT) controller against various dynamic EV load scenarios is carried out, and the results are validated through a hardware-in-loop experimental setup. Despite the intermittent source and EV load dynamics, the proposed piece-wise droop control can maintain voltage regulation with less than 1% deviation.