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• Experimentally-validated model to predict size and distribution of nanoparticles. • Model couples time-resolved fluid dynamic simulations with population balance. • Increasing mixing rate, increases overall nanoparticle synthesis but not local rate. • Combination of fast mixing and high rate leads to a burst of nuclei formation. Achieving size control during nanomaterial synthesis is critical for their deployment in numerous applications due to their strong size-properties relationships. However, it remains a challenge in the field due to multi-step mechanisms in nanoparticle synthesis. In this paper, we present an experimentally-validated model able to predict the particle size and distribution of silver nanoparticles synthesised in flow reactors by coupling time-resolved fluid dynamic simulations with population balance. The model reveals fundamental correlations between reactor design and the corresponding size of the nanoparticles. It shows that increasing mixing rate, increases the overall rate of nanoparticle formation, where nanoparticle synthesis takes place homogeneously in the whole of the reaction volume. In contrast, when there is a distinguishable interface between reactant streams (i.e. low mixing rate), nucleation and growth take place in a small fraction of the reaction volume in the interface between the inlet streams. The combination of fast mixing and high average nucleation rate can lead to a burst of nuclei formation translated in small size and narrow size distribution. This work presents a new approach for the use of computational-guided design of reactors for the synthesis of nanomaterials to move the field away from current trial-and-error strategies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Effect of process parameters on DAC productivity and specific energy requirement. • A dynamic TVSA process model with the effect of humidity on CO 2 adsorption was used. • Optimizing cycle step durations is critical for improving DAC performance. • DAC cost-effectiveness depends on operating parameters, weather, and unit location. • Optimizing parameters requires balancing performance and constraints. Direct air capture (DAC) of CO 2 through adsorption is a promising technology for mitigating climate change, but its high cost presents a significant challenge to large-scale implementation. To address this issue, this study presents a dynamic fixed-bed model of the closed-inlet temperature-vacuum swing adsorption (TVSA) process and investigates the impact of various operating and model parameters on DAC performance via dynamic simulation. The results indicate that optimizing the durations of the adsorption and regeneration phases is crucial for improving cyclic DAC performance. Maximizing the working capacity by driving both phases towards near-equilibrium conditions generally leads to the lowest specific energy requirement (SER), while cutting the adsorption phase earlier increases the CO 2 productivity. Additionally, appropriate choices of operating parameters, such as feed gas velocity and vacuum pressure, can significantly improve DAC process performance. Furthermore, placing the DAC unit in a location with favourable temperature and humidity conditions, affordable heat source, and elevated CO 2 concentration can greatly enhance the process and its cost-effectiveness. These individual approaches have the potential to multiply productivity and decrease SER by increasing the working capacity, shortening the cycle duration, and minimizing the absolute energy consumption. However, optimizing the DAC process necessitates careful consideration of trade-offs between productivity, SER, and various constraints. These insights, along with the developed model, can provide a valuable basis for further advancement of DAC technology. [ABSTRACT FROM AUTHOR]

• An intelligent risk assessment method is proposed to capture potential risks. • LSTM trained to provide predictions without mechanistic understanding. • The average MAPE of risk variables predicted by LSTM is only 0.6356%. • Cooling water failure and controller failure have a higher risk level. • Level controller failure has more serious harm in a short time. Safety assessment is a prerequisite for the normal operation of chemical process. Although it can reduce the frequency of accidents, it relies heavily on the experience of experts and is difficult to assess potential accident risks caused by dynamical chemical conditions. To address this problem, a novel intelligent quantitative risk assessment method (DYN-LSTM-QRA) is proposed based on dynamic mechanism model for ammonia synthesis process in this paper. The process under study is first simulated by Aspen Dynamics (DYN) to obtain dynamic data set under abnormal conditions. Then, the potential relationships between variables are deeply learnt by long short-term memory (LSTM) to represent complex mechanism relationship among predicted variables that have a direct impact on the hazard severity of accident. Finally, a quantitative risk assessment (QRA) method based on variable prediction is given to assess the risk of multiple conditions and their dynamic hazard range. The reliable control plans are also proposed to ensure the safe running of the process. The proposed method was applied to the leakage accident of the ammonia synthesis process, which effectiveness and reliability are proven by dynamic simulation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermochemical heat storage using Ca(OH) 2 /CaO in a fluidized bed reactor was modeled. • Variable renewable energy fluctuation can be absorbed by using steam as fluidizing gas. • The energy efficiencies for nitrogen were higher than that for steam. • The simulated data agreed with the experimental data, albeit not perfectly. A simplified dynamic model of a Ca(OH) 2 /CaO–containing fluidized bed reactor was developed by combining a continuously stirred tank reactor model in the solid phase with a series of continuously stirred tank reactors in the gas phase for mass transport. The heat supplied to the thermochemical heat storage system was allowed to fluctuate to evaluate the absorption of variable renewable energy fluctuation. In addition, the performance of the fluidized bed was assessed using nitrogen or steam as the fluidizing gas. For nitrogen, the fluctuation of bed temperature increased with the increasing time step of heat change. The bed temperature was affected by the magnitude of the fluctuation of the supplied heat more strongly for nitrogen than for steam, mainly because the rate of dehydration under these conditions was more strongly dependent on temperature than in the case of steam. The thermochemical heat storage efficiency (calculated by considering reaction heat) and energy storage efficiency (calculated by considering reaction heat and sensible heat) equaled 14.1% and 34.1% for steam and 29.9% and 62.7% for nitrogen, respectively. The differences between the efficiencies for steam and nitrogen were ascribed to the latent heat of H 2 O. Sensitivity analysis showed that both efficiencies increased with increasing heat supply because of the concomitant decrease in the time required to heat the system to the reaction temperature. During this time, thermochemical heat storage did not occur, which resulted in lower efficiency. Therefore, the fluctuation from variable renewable energy can be absorbed by using thermochemical heat storage. [ABSTRACT FROM AUTHOR]