ChemEngineering, Volume 8, Issue 3 (June 2024) – 6 articles

In this study, TiO₂ nanospheres (TiO₂-NS) were synthesized by the solvothermal method. Firstly, the synthesized nanomaterial was characterized by X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR), scanning electron microscopy (SEM) and UV-Vis Diffuse Reflectance Spectroscopy (DRS). To study the photocatalytic degradation of Tartrazine (TTZ) and Naphthol Blue Black (NBB) in a binary mixture, the influence of some key parameters such as pH, pollutant concentration and catalyst dose was taken into account under visible and UV light. The results show a 100% degradation efficiency for TTZ after 150 min of UV irradiation and 57% under visible irradiation at 180 min. The kinetic study showed a good pseudo-first-order fit to the Langmuir–Hinshelwood model. Furthermore, in order to get closer to the real conditions of textile wastewater, the influence of the presence of salt on TiO₂-NS’s photocatalytic performance was explored by employing NaCl as an inorganic ion. The optimum conditions provided by the Response Surface Methodology (RSM) were low concentrations of TTZ (2 ppm) and NBB (2.33 ppm) and negligible salt (NaCl) interference. The percentage of photodegradation was high at low pollutant and NaCl concentrations. However, this yield became very low as NaCl concentrations increased. The photocatalytic treatment leads to 31% and 53% of mineralization yield after 1 and 3 h of visible light irradiation. The synthesis of TiO₂-NS provides new insights that will help to develop an efficient photocatalysts for the remediation of contaminated water. Full article

The solid volume fraction of a slurry requiring solid–liquid separation often fluctuates in industrial cake filtration processes. For low solid volume fractions, particle segregation arises, resulting in an inhomogeneous filter cake structure. Particle segregation has significant impacts on cake formation such as a longer cake formation time compared to homogeneous cakes. This work addresses the impact of this effect on vibration compaction, which is an alternative deliquoring method applying oscillatory shears to the filter cake. The dewatering results of homogeneous and segregated cakes made of the same material with a broad particle size distribution are compared. Although cake deliquoring is achievable despite particle segregation, vibration compaction is more effective for homogeneous cakes. The reason is that no particle size homogenization within segregated cakes occurs due to oscillatory shear, as particle size analyses indicate. The particle size measurements of cakes before and after vibration compaction reveal that the material’s particle size distribution is preserved despite vibration application. Vibration compaction achieves higher deliquoring than the common compaction method by squeezing, as elastic recovery effects after squeezing lead to the reabsorbing of liquid, already expressed and stored in the filter cloth. This demonstrates that vibration compaction is a real alternative for cake deliquoring. Full article

Solid–liquid separation plays a decisive role in various industrial applications particularly in the treatment and purification of suspensions. Solid bowl centrifuges, such as the decanter centrifuge, are commonly employed in these processes as they operate continuously and enable high throughputs with short processing times. However, predicting the separation performance of solid bowl centrifuges proves to be challenging due to dynamic phenomena within the apparatus, such as particle settling, sediment build-up, consolidation and sediment transport. In practice, design considerations and the dimensioning of the apparatus rely on analytical models and the manufacturer’s expertise. Computational Fluid Dynamics (CFD) offers a way to deepen our understanding of these devices by allowing detailed examination of flow phenomena and their influence on the separation processes. This study utilizes the open-source software OpenFOAM to simulate multiphase flow in a laboratory-scale decanter centrifuge, solving individual transport equations for each particle size class. The basis is the characterization of the material through targeted laboratory experiments to derive material functions that describe the hindered settling and the sediment consolidation. Furthermore, experiments on a laboratory decanter served as validation. The results demonstrate the solver’s capability to replicate clarification and classification within the apparatus. Furthermore, the solver supports the definition of geometries tailored to specific separation tasks. This research demonstrates the potential of CFD for a better understanding of complex centrifuge processes and for optimizing their design to improve performance. Full article

A straightforward, fast and efficient analytical method was developed which utilizes a magnetic composite called three-dimensional graphene (3D-G@Fe₃O₄) as an adsorbent to recover nitrite ions (NO₂⁻) from environmental water samples. The investigation into the synthesized adsorbent contained an examination of its morphology, chemical composition, structural attributes, and magnetic properties. This comprehensive analysis was conducted using various instrumental techniques, including Fourier transform-infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), Raman spectroscopy, X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), Barrett-Joyner-Halenda (BJH), and vibrating sample magnetometry (VSM). The adsorbent surface was activated by adding cetyltrimethylammonium bromide (CTAB) to the sample solution. To improve the selectivity and sensitivity of the method, nitrite ions were reacted with sulfanilic acid and chromotropic acid sequentially. An orange-red azo-dye complex was formed in the presence of nitrite ions with a clear absorbance peak at 514 nm. The effect of the main experimental parameters such as the pH of the sample solution, adsorbent dosage, and CTAB dosage was explored, and the optimization process was performed using a central composite design (CCD). The linear dynamic range (20–100 ng mL⁻¹) was determined under optimal experimental circumstances, yielding a reasonable determination coefficient (R2, 0.9993), a detection limit of 5.12 ng mL⁻¹, an enrichment factor of 167, and precision values of 1.0% intraday and 2.9% inter-day. The methodology successfully identified minute nitrite ions in environmental water samples with relative recoveries that varied between 96.05 and 101.6 ng mL⁻¹. Full article

Prospective environmental and technological assessment of emerging chemical processes is necessary to identify, analyze and evaluate the technologies that are highly imperative in the transition towards climate neutrality. The investigation of the environmental impacts and material and energy requirements of the processes at the low technology readiness level (TRL) is important in making early decisions about the feasibility of adapting and upscaling the process to the industrial level. However, the upscaling of new chemical processes has always been a major challenge; and in this context, there is no general methodological guidance available in the literature. Hence, a new comprehensive methodological framework for upscaling of novel chemical processes is designed and presented based on thermodynamic process modeling and simulation. The practical implementation of the proposed methodology is extensively discussed by developing a scaled-up novel carbon capture and utilization (CCU) process comprised of sequestration of carbon dioxide (CO₂) from blast furnace gas with a capacity of 1000 liter per hour (L/h) using methanol and its utilization as a precursor to produce methane (CH₄). It was found that thermodynamic process modeling and simulations based on the perturbed-chain statistical associating (PC-SAFT) equation of state (EOS) can precisely estimate the CO₂ solubility in methanol and conversion to CH₄ at various temperature and pressure conditions. The achieved thermophysical property and kinetics parameters can be employed in process simulations to estimate scaled-up environmental flows and material and energy requirements of the process. Full article

Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods. Full article