Band 19, Heft 5

This paper aims to investigate the energy cost of eliminating Cr(VI) by nanofiltration membranes (NF). The modified pore flow model was utilized to predict the performance of NF membrane in terms of ion retention and water productivity. Then, the energy cost was estimated according to the obtained results from this model as well as available data from literature. The effect of feed flow, applied pressure, and temperature on energy cost were highlighted and thoroughly assessed. It is shown that high retention values can be achieved with relatively high energy cost. Nonetheless, energy cost of 0.04 $/m 3 have been estimated for Cr retention less than 95 % at a pressure of 5 bar. It is also assessed that feed flow has a significant influence on energy cost relative to other operating parameters. In particular, increasing feed flow from 40 to 760 L/h leading the energy cost to be increased by twelve times. At high feed flow, however, energy cost can be largely reduced by applying pressure higher than 10 bar. It is concluded that high feed temperature is favored if there is no need for heating equipment.

This study pioneers a method for forecasting hydrogen and nitrogen quantities in gasification processes, which is crucial for transforming carbon-based materials into valuable gases with minimal environmental impact. It addresses the pressing need for precise and economical solutions in gasification by streamlining estimation procedures across various operational conditions. Anchored in historical data, the K-nearest neighbour’s (KNN) model forms the core of this method, adeptly capturing intricate relationships between input variables and gas production results. What sets this research apart is the integration of advanced optimization techniques, Beluga whale optimization (BWO), and Northern Goshawk Optimization (NGO), further refining the accuracy of the KNN model’s predictions. This work signifies a substantial stride in optimizing gasification processes, offering a pathway towards more efficient and sustainable conversion of carbon-based materials, showcasing the potential of data-driven sustainability in this domain, and ultimately diminishing the environmental impact of these operations. Results highlight the outstanding predictive performance of the KNNG model, achieving an impressive R 2 value of 0.995 and 0.994 during training. Notably, both the KNNG and KNBW models outperformed the basic KNN model in forecasting gasification outputs, underscoring the reliability and effectiveness of these optimized models in predicting these processes.

In the contemporary era, marked by the increasing significance of sustainable energy sources, biomass gasification emerges as a highly promising technology for converting organic materials into valuable fuel, offering an environmentally friendly approach that not only mitigates waste but also addresses the growing energy demands. However, the effectiveness of biomass gasification is intricately tied to its predictability and efficiency, presenting a substantial challenge in achieving optimal operational parameters for this complex process. It is at this precise juncture that machine learning assumes a pivotal role, initiating a transformative paradigm shift in the approach to biomass gasification. This article delves into the convergence of machine learning and the prediction of biomass gasification and introduces two innovative hybrid models that amalgamate the Support Vector Regression (SVR) algorithm with Coot Optimization Algorithm (COA) and Walrus Optimization Algorithm (WaOA). These models harness nearby biomass data to forecast the elemental compositions of CH 4 and C 2 H n , thereby enhancing the precision and practicality of biomass gasification predictions, offering potential solutions to the intricate challenges within the domain. The SVWO model (SVR optimized with WaOA) is an effective tool for predicting these elemental compositions. SVWO exhibited outstanding performance with notable R 2 values of 0.992 for CH 4 and 0.994 for C 2 H n , emphasizing its exceptional accuracy. Additionally, the minimal RMSE values of 0.317 for CH 4 and 0.136 for C 2 H n underscore the precision of SVWO. This accuracy in SVWO’s predictions affirms its suitability for practical, real-world applications.

The worldwide transition to cleaner, sustainable energy sources, prompted by population growth and industrialization, responds to uncertain fossil fuel prices and environmental concerns, highlighting the substantial benefits of renewable energy in reducing greenhouse gas emissions and addressing climate change. Derived from non-fossilized organic materials, biomass emerges as a significant and sustainable contributor to renewable energy. Its diverse nature is complemented by a range of conversion technologies, encompassing combustion, pyrolysis, gasification, and liquefaction, providing versatile avenues for biomass energy transformation. Gasification, the transformative process of converting organic matter into combustible gases under controlled oxygen levels, is accomplished through direct oxygen supply or pyrolysis. This method yields a dependable gaseous fuel versatile for heating, industrial processes, power generation, and liquid fuel production. Machine learning employs advanced statistical techniques for modeling across diverse industries, showcasing particular efficacy in optimizing thermochemical processes by precisely identifying the optimal operating conditions required for achieving desired product properties. These models utilize proximate biomass data to predict the elemental compositions of N 2 and H 2 . Assessment of both single and two hybrid models indicated that the introduced optimizers significantly enhanced the estimation of N 2 and H 2 when combined with Decision Tree (DT), with Decision Tree Coupled with Artificial Hummingbird Algorithm (DTAH) proving particularly effective. Notably, DTAH demonstrated outstanding performance with remarkable R 2 values of 0.990 for N 2 and 0.992 for H 2 . Additionally, the minimal Root Mean Square Error (RMSE) values of 1.291 and 1.550 for N 2 and H 2 predictions respectively underscore the precision of DTAH, establishing it as a suitable choice for practical real-world applications.

Ethylene glycol (EG) is a valuable commodity organic intermediate that is produced using the catalyzed gas-phase hydrogenation process of dimethyl oxalate (DMO) from syngas. The reactor process is challenging to control because of its nonlinearity and multivariable condition. Thus, this study proposes the application of Neural Wiener model predictive control (NWMPC) for DMO hydrogenation reactor control. The application of empirical-based MPC, such as NWMPC, is still new in DMO hydrogenation reactor control. In order to simulate the process, the DMO hydrogenation reactor is modeled using Aspen Plus and Aspen Dynamic software. The Neural Wiener (NW) model is developed based on state space and neural network modeling using a Linear-Nonlinear (L-N) identification approach. A validation test is also performed to verify the accuracy of the NW model. Based on the test, the model accuracy is acceptable with the coefficient of determination (R 2 ) of 0.965 for EG output mole fraction (first output) and R 2 of 0.936 for product temperature (second output). The NWMPC capability is evaluated with a PID controller to handle a setpoint change in EG output mole fraction and reject disturbance in the feed stream flow rate. The control performance results have demonstrated the superior ability of the NWMPC to handle such scenarios better than PID in terms of controller action speed and profile.

Certain manufacturing or industrial processes may involve variable conditions, and intentionally transient boiler operation allows optimal adaptation to these variations. This helps maintain efficiency and reduce energy consumption during different process phases. Transient operation is inherent during the start-up and intermittent phases in reaching the pressure required for boiler operation. Optimizing these transient states can reduce energy consumption. Dynamic optimization of boilers is crucial for several reasons, especially in industrial and power generation settings. Boilers are used to produce steam or hot water for various processes, and optimizing their performance can lead to increased efficiency, reduced energy consumption, and improved overall system reliability. The dynamic optimization problems were solved using the orthogonal collocation method. Three problem optimizations were considered in this study: minimize process time ( P 1), minimize energy consumption without optimized final time ( P 2), minimize energy consumption with optimized final time ( P 3). The control/decision variables applied were firing rate, Q , and water feed flowrate, q f . From the simulation results, the control trajectories of P 3 were chosen to be the most effective control operation to achieve the minimum energy consumption for reaching target pressure, i.e., 10.2 MPa, with a reasonable intermittent unit time. In practice, the selection of the number of intervals is often determined through a combination of domain knowledge, computational resources, and the desired level of accuracy. Sensitivity analysis and testing with different interval sizes can help in understanding the impact of this parameter on the optimization results. A greater interval time will decrease energy consumption.

Gas–solid fluidized bed reactors exhibit improved heat and mass transfer performance as compared to packed beds. Corrugated walls installed in narrow gas–solid bubbling fluidized bed (CWBFB) enclosures have been observed to decrease minimum bubbling velocity, reduce bubble size, improve gas distribution, provide stable operation, and minimize particle carryover or loss. Thorough analyses of the wall-to-bed heat transfer coefficient in flat- (FWBFB) and corrugated- (CWBFB) wall bubbling fluidized beds have been performed for a variety of operating conditions and geometric parameters. Fast-response self-adhesive heat flux probes and thermocouples were used to simultaneously measure the wall-to-bed heat flux, surface and bed temperatures, and were used to determine the heat transfer coefficient ( HTC ) at various axial and lateral locations. For a given set of parameters, a significant increase in HTC was observed at lower gas flow rates in CWBFB as compared to FWBFB. It was shown that CWBFB inventory required lower U mb (gas flow rate) as compared to FWBFB. Full 3-D transient Euler–Euler CFD simulations using the kinetic theory of granular flow were also performed, which confirmed the experimental results.

Methanol (CH 3 OH) is a versatile compound used in various industries. Catalytic reactors used in CH 3 OH production are expensive due to high energy and raw material costs. Multi-objective optimization (MOO) is used to optimize CH 3 OH production, but it is still lacking. Researchers use alternative strategies or modify existing ones to achieve better results. This study applied model-based optimization using an ASPEN Plus simulator and Multi-objective Ant Lion Optimization (MOALO) to address the issue. The results revealed the highest conversion and product rate, with the lowest energy cost, side product, and bare module cost, CBM. The decision variable plots indicate that the reactor’s pressure significantly affects the optimal solution. This study provides valuable insight into optimizing CH 3 OH production.

In this paper, we investigate the impact of an inclined magnetic field of uniform intensity on viscous, incompressible pressure-driven Stokes flow through a slip-patterned, rectangular microchannel using the boundary element method based on the stream function-vorticity variables approach. The present investigation focuses only on the out-phase slip patterning of the microchannel walls. We address two scenarios of slip patterning, specifically large and fine slip patterning, which are determined by the periodicity of the patterning. We utilized the no-slip and Navier’s slip boundary conditions in an alternative manner on the walls. The Stokes equations govern the viscous flow through a microchannel. We assume a very small magnetic Reynold’s number to eliminate the equation of induced magnetic field in the present study. We analyzed the impact of considered dimensionless hydrodynamic parameters, including the Hartman number ( Ha ), inclination angle ( θ )  of the magnetic field, and the slip length ( l s )  on fluid dynamics. In the case of fine slip, we observed significant variations in both velocity and pressure gradient, in contrast to large slip patterning. Fine slip patterning significantly increases the shear stress at slip regimes, while large slip periodicity significantly reduces it at no-slip regimes. The present investigation has several notable implications, such as regulation and advancement of mixing and heat transmission within microfluidic systems.

Turmeric is commonly acknowledged in traditional medical practices for its strong healing properties. In the present work, hydro-distillation was employed to extract essential oils from turmeric powder. The response surface methodology (RSM) was applied to investigate the effects of various parameters, including evaporation rate (0.23, 0.5, 0.8, 0.97, 1.36, 2 ml/min), solid/liquid ratio (4:100, 6:100, 8:100, 1:10, 11:100 g/ml), and extraction duration (13–250 min) on the yield of essential oils. The central composite design (CCD) proved to be an effective tool for evaluating the extraction yield of essential oils. A three-layer artificial neural network (ANN) was utilized to develop the extraction model, employing the Levenberg–Marquardt (LM) optimization algorithm. The neural network’s input layer comprised the solid/liquid ratio, evaporation rate, and extraction time, while the output layer indicated the yield of essential oil extraction. The most appropriate model included a hidden layer with 16 neurons, achieving R 2 and MSE values of 0.9989 and 0.0013, respectively. This investigation indicates that an artificial neural network prediction model serves as an effective method for estimating essential oil yield.