Enhancement of process modelling and simulation evaluation by deploying a test for assessment and feedback individualisation

This paper demonstrates a novel approach for a computer-based course assessment. A test is introduced in which computers are deployed. This significantly contributes to the enhancement of the marking consistency, individual performance distinction and feedbacks, and widen the questions range for computer-based modules. The proposed test method, for the first time, uses the simulation files marking for individualised evaluation purposes. The methodology has successfully been implemented in practice for three modules including Process Simulation (CE2105), Advanced Process Simulation (CE4023), and Process Computation (CE3021) at Aston University (UK) over three academic years, from 2016 to 2019. The effectiveness of the proposed approach has been evaluated using several factors, including final marks, consistency multiple academic years, and mark distribution. In contrast to the common teamwork assessments, individualised feedback became possible. While ASPEN has been used for CE4023 and CE2105 tests, MATLAB has been applied as the computation platform for CE3021 module. This reveals the applicability of different software in proposed methodology. The number of students in the cohorts studied was from 52 to 204, demonstrating the applicability of the method for various cohort sizes. Even though the methodology has been demonstrated based on the chemical engineering discipline modules, it allows digitalising the delivery and assessment of a wide range of simulation techniques in many disciplines.     

Statistical optimization of operating parameters of microbial electrolysis cell treating dairy industry wastewater using quadratic model to enhance energy generation

The performance of Microbial electrolysis cell (MEC) is affected by several operating conditions. Therefore, in the present study, an optimization study was done to determine the working efficiency of MEC in terms of COD (chemical oxygen demand) removal, hydrogen and current generation. Optimization was carried out using a quadratic mathematical model of response surface methodology (RSM). Thirteen sets of experimental runs were performed to optimize the applied voltage and hydraulic retention time (HRT) of single chambered batch fed MEC operated with dairy industry wastewater. The operating conditions (i.e) an applied voltage of 0.8 V and HRT of 2 days that showed a maximum COD removal response was chosen for further studies. The MEC operated at optimized condition (HRT- 2 days and applied voltage- 0.8 V) showed a COD removal efficiency of 95 ± 2%, hydrogen generation of 32 ± 5 mL/L/d, Power density of 152 mW/cm² and current generation of 19 mA. The results of the study implied that RSM, with its high degree of accuracy can be a reliable tool for optimizing the process of wastewater treatment. Also, dairy industry wastewater can be considered to be a potential source to generate hydrogen and energy through MEC at short HRT.     

MHD mixed convection of a Cu–water nanofluid flow through a channel with an open trapezoidal cavity and an elliptical obstacle

In the current work, numerical simulations are achieved to study the properties and the characteristics of fluid flow and heat transfer of (Cu–water) nanofluid under the magnetohydrodynamic effects in a horizontal rectangular canal with an open trapezoidal enclosure and an elliptical obstacle. The cavity lower wall is grooved and represents the heat source while the obstacle represents a stationary cold wall. On the other hand, the rest of the walls are considered adiabatic. The governing equations for this investigation are formulated, nondimensionalized, and then solved by Galerkin finite element approach. The numerical findings were examined across a wide range of Richardson number (0.1 ≤ Ri ≤ 10), Reynolds number (1 ≤ Re ≤ 125), Hartmann number (0 ≤ Ha ≤ 100), and volume fraction of nanofluid (0 ≤ φ ≤ 0.05). The current study's findings demonstrate that the flow strength increases inversely as the Reynolds number rises, which pushes the isotherms down to the lower part of the trapezoidal cavity. The Nu_avg rises as the Ri rise, the maximum Nu_avg = 10.345 at Ri = 10, Re = 50, ϕ = 0.05, and Ha = 0; however, it reduces with increasing Hartmann number. Also, it increase by increasing ϕ, at Ri = 10, the Nu_avg increased by 8.44% when the volume fraction of nanofluid increased from (ϕ = 0–0.05).     

Ni–Cr alloys for effectively enhancing hydrogen evolution processes in phosphate-buffered neutral electrolytes

Significant efforts have been made to develop highly active non-noble metal-based, affordable metallic and stable electro-catalysts for hydrogen evolution reaction (HER). Strong acid and bases are now used in HER operations to achieve large-scale, sustained H₂ fuel production. However, few studies have utilized phosphate-buffered neutral electrolytes (PBS) in the field of neutral electrolyte technology. In this work, a certain alloys with a Ni–Cr basis have been produced as favorable components for the HER under neutral conditions. Additionally, the current investigations are emphasizing on the concentration of buffer phosphate species in the HER activity of various materials. By employing polarization and electrochemical impedance spectroscopy (EIS) in neutral solutions, the electro-catalytic activity of new alloys on HER was evaluated. According to the preliminary findings, the examined Ni–Cr-based alloys show superior HER catalytic activity in neutral electrolytes. Additionally, the Ni–Cr alloy matrix with Fe and Mo added enhances HER electrocatalytic efficiency while lowering interfacial charge transfer resistance. Due to its low overpotential of ?297 mV @ 10 mA cm^?2 and Tafel slope of 94 mV dec^?1 in 1.0 M PBS media, the Ni–Cr–Mo–Fe alloy exhibits an efficient HER, suggesting that the Ni–Cr–Mo–Fe electrode will be a potential noble metal-free electro-catalyst for HER. The Ni–Cr–Mo–Fe cathode is a readily available and affordable material for the production of HER in neutral medium.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

The doping of SZC solders with bismuth to improve their thermal and tensile characteristics for microelectronic applications

Journal of Materials Science: Materials in Electronics - Sn–Zn–Cu is one of the most popular soldering alloys, especially when it has been doped with Bi, which has proved very...     

Chemically complex intermetallic alloys: A new frontier for innovative structural materials

Intermetallic materials are bestowed by diverse ordered superlattice structures together with many unusual properties. In particular, the advent of chemically complex intermetallic alloys (CCIMAs) has received considerable attention in recent years and offers a new paradigm to develop novel metallic materials for advanced structural applications. These newly emerged CCIMAs exhibit synergistic modulations of structural and chemical features, such as self-assembled long-range close-packed ordering, complex sublattice occupancy, and interfacial disordered nanoscale layer, potentially allowing for superb physical and mechanical properties that are unmatched in conventional metallic materials. In this paper, we critically review the historical developments and recent advances in ordered intermetallic materials from the simple binary to chemically complex alloy systems. We are focused on the unique multicomponent superlattice microstructures, nanoscale grain-boundary segregation, and disordering, as well as the various extraordinary mechanical and functional properties of these newly developed CCIMAs. Finally, perspectives on the future research orientation, challenges, and opportunities of this new frontier are provided.     

Enhanced photocatalytic and antifungal activity of ZnO–Cu2+and Ag@ZnO–Cu2+ materials

Zinc oxide is one of the most versatile nanostructured materials with a broad range of applications. Besides, its physicochemical properties can be tuned easily by synthesis conditions to be optimal for a specific application. In our group, we aim for the production of visible light-active materials with enhanced antimicrobial activity. Thus, we synthesize ZnO–Cu²⁺and Ag@ZnO–Cu²⁺ by using a fast and robust microwave solvothermal reaction. We investigate the limit of solubility of Cu²⁺into ZnO lattice producing Cu doped ZnO materials with different doping levels (1, 2, 3, 4, and 5 at. %, Cu/Zn). We also investigate the role of the copper precursor, using copper(II) acetate or copper(II) sulfate as model precursors. Copper acetate incorporates more efficiently into ZnO lattice by decreasing the Eg value of the doped materials at low doping levels. Furthermore, we study the composites Ag@ZnO–Cu²⁺ aiming to reduce doping levels and to improve antimicrobial activity. Characterization of the materials by different techniques demonstrates their uniform size, purity, crystallinity, and visible light activity. In this study, we evaluate airborne fungal contamination and demonstrate the capacity of ZnO–Cu²⁺ and Ag@ZnO–Cu²⁺ to inhibit fungal growth. We studied the microbiological quality of indoor air (vivarium) by sampling air under different conditions. By sampling air with a photocatalytic prototype, the amount of fungi in the air decreases considerably, particularly fungi that can enter the lung. In addition, ZnO–Cu²⁺ shows excellent antifungal activity against Candida sp at low doses. We use Atomic force microscopy (AFM) and holotomographic microscopy (HTM) to provide further evidence on the capacity of the prepared materials to achieve effective damage to fungal cells and to inhibit biofilm formation.