Pilot-scale Data Research Articles

Data-driven parameterization and development of mechanistic cell cultivation models in monoclonal antibody production processes: Shifts in cell metabolic behavior

Representative kinetic models to describe monoclonal antibody (mAb) production processes are needed for effective process design. The development of mechanistic models can be impeded by the lack of complete understanding of changes in cell metabolism, e.g., lactate metabolic shifts. State-estimation-based methods were applied to assess the fit of available kinetic models over experimental runs. The results indicated the regions where model parameter updates were required. Different clustering strategies were applied to isolate the variations in the culture environment and correlate them to the lactate shifts. Alternative formulations for the specific lactate consumption/production term were provided for each identified phase. Two case studies are presented for pilot-scale data in different reactor types. The results show the improvement in modeling accuracy and highlight the role of oxygen and nutrient levels on the shifts. The approach showcases the use of data-driven insights to effectively utilize limited experimental data to develop robust mechanistic models.

Analysis and Prediction of the Leaching Process of Ionic Rare Earth: A Data Mining Study with Scarce Data

To unveil the impact of each condition variable on the leaching efficiency index during the heap leaching process of rare earth ore and establish a prediction model for leaching conditions and efficiency, common parameters in the heap leaching process of rare earth ore were selected. In addition, the pilot-scale test data were collected over 50 days. Based on the collected data, the Ordinary Least Squares (OLS) linear regression method was used for fitting analysis to determine each variable’s influence on the change in leaching efficiency. The results indicated a linear relationship between the flow rate of the leaching solution and leaching efficiency. In contrast, no obvious linear relationship was observed between other condition variables and leaching efficiency. Spearman’s rank correlation coefficient was calculated to analyze the nonlinear correlation between the abovementioned variables and the leaching efficiency index. The correlation coefficients were found to be −0.78, 0.88, −0.93, −0.53, 0.71, and −0.93 for ammonium content in the leaching agent, pH of the leaching agent, rare earth content, ammonium content in the leaching solution, pH of the leaching solution, and the flow rate of the leaching solution, respectively. This suggests that the flow rate of the leaching solution, rare earth content, and pH of the leaching agent significantly influence leaching efficiency, thus affecting the rare earth leaching efficiency index. Based on the correlation analysis results of leaching conditions and efficiency, a dataset with limited data trained by the common Ordinary Least Squares model, linear regression model, random forest model, and support vector machine regression model was selected to develop a prediction model for the leaching process data. The results indicated that the random forest model had the lowest mean square error of 7.47 among the four models and the coefficient of determination closest to 1 (0.99). This model can effectively analyze and predict condition variables’ data and leaching efficiency index in the heap leaching process of rare earth ore, with a prediction accuracy exceeding 90%, thus providing intelligent guidance for the heap leaching process of rare earth ores.

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation.

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with anormalized root mean squared error(N-RMSE)of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

HS3 as a novel solvent for carbon capture: Model validation and an industrial case study with comparison against 30 wt% MEA

CO2 capture is currently the most mature technological solution to reduce the environmental footprint of several emitters. However, the characterization of innovative blends with reduced energy demand and environmental impact is nowadays a challenge to increase the profitability and deployment of carbon capture on an industrial scale. This work investigates an innovative non-proprietary amine blend, called HS3. This article presents the development of a full model (including thermodynamics, kinetics, and mass transfer) and its validation with pilot-scale data covering temperature, CO2 concentration, and capture rate ranges of interest for industrial applications. The model predicts the main process Key Performance Indicators, such as CO2 captured, stripped flow, and cycling capacity, with deviations from the measurements lower than 7%. Then, the validated model is exploited for sizing and designing a carbon capture process from the flue gas generated within an oil refinery. A special focus is devoted to energy integration and process optimization by means of a sensitivity analysis. Eventually, HS3 performances are compared to benchmark MEA in terms of energy requirements and unit operation sizing for the same case study. Results show that HS3 can reduce the specific reboiler duty (MJ/kg CO2 captured) and the required solvent flow per unit of flue gas (kg/kg) by 21% and 19%, respectively. Examples of comprehensive models developed starting from lab-scale testing up to the validation on a pilot scale are still limited in the literature. The validation in a semi-industrial pilot is needed to fully understand the solvent properties including, for instance, drawbacks which cannot be detected on a small scale. Needless to mention, reliable models are needed for consistent scale-up, techno-economic assessment, and LCA analysis. Thus, this present presents a model validation using semi-industrial pilot data and measurements, which is not common in the literature.

Process evaluation of mild hydrothermal carbonization to convert wet biomass residue streams into intermediate bioenergy carriers

Hydrothermal carbonization (HTC) is a promising process for the upgrading of wet biomass residues. Models of HTC processes, in particular at continuous pilot-scale, are needed to move HTC from lab-scale to industrial scale. This study presents a process model for mild HTC, dewatering and conversion to intermediate energy carriers (bio-pellets and biogas for power and/or heat production) of three wet biomass residue streams: paper sludge, olive pomace and orange peels, based on lab- and pilot-scale experiments. In addition, the process energy efficiency and feedstock utilization of the HTC process is calculated and compared with conventional treatment options for the chosen residues, i.e., direct anaerobic digestion (olive pomace, orange peels) or combustion after conventional dewatering (paper sludge). The process model indicates that the HTC pilot-scale process is much more efficient in terms of feedstock utilization to produce heat and/or power than the reference scenarios. The process energy efficiency of the HTC process (pilot-scale) was calculated to be 26 %, 63 % and 40 % for paper sludge, olive pomace and orange peel feedstocks, respectively. For all feedstocks, both the solid and liquid-generated products are equally important for improving the overall process energy efficiency. This study demonstrates the potential benefits of HTC processes for upgrading wet biomass waste streams based on continuous pilot-scale data.

Removal of Per- and Polyfluoroalkyl substances by anion exchange resins: Scale-up of rapid small-scale column test data

Anion exchange (IX) is a readily implementable water treatment method that can effectively remove per- and polyfluoroalkyl substances (PFAS). The overarching objective of this research was to predict PFAS removal in full- or pilot-scale packed-bed IX resin contactors from rapid small-scale column test (RSSCT) data. Specific objectives were to (1) assess the effects of IX resin crushing on total anion exchange capacity and packed bed density, (2) determine the effects of initial PFAS concentration on PFAS uptake capacity, (3) determine the rate-limiting step controlling PFAS uptake kinetics, (4) determine the effects of hydraulic loading rate on PFAS uptake capacity, and (5) link constant diffusivity RSSCT data to pilot test data to develop a scale-up protocol. Experiments were conducted with two single-use IX resins and three water matrices, including coagulated surface water and groundwater.Crushing IX resin did not substantially change the bed density and total anion exchange capacity, but the morphology of particles changed from almost perfectly spherical to irregularly shaped. PFAS uptake capacity was independent of influent PFAS concentrations in the 30–300 ng/L range. This finding facilitated the development of an RSSCT scale-up approach because influent PFAS concentrations in RSSCTs and corresponding pilot tests often differ. Biot number values and data from interrupted RSSCTs demonstrated that film diffusion or a combination of film diffusion and intraparticle diffusion controls the rate of PFAS uptake by IX resins. From RSSCTs with identical empty bed contact times but different hydraulic loading rates (vf), PFAS uptake capacity was found to be a function of the square root of the product of Sherwood number and particle shape factor (Sh×ϕ). Using a constant diffusivity RSSCT design with a reduced vf, full- or pilot-scale PFAS breakthrough data can therefore be predicted by multiplying the bed volumes of water treated in the RSSCT by a factor of (Shpilot×ϕpilot)/(ShRSSCT×ϕRSSCT) . This research will support the design of future IX treatment processes in the context of PFAS remediation and drinking water treatment.

Comparative life cycle analysis of PHA-based consumer items for daily use

USABLE Packaging project has developed an innovative value chain based on mixed microbial cultures using organic wastes as feedstock to produce prototypes of PHA-based items that were compared to their commercial counterparts, so the expected better environmental performance can be checked. To do so, a cradle-to-grave life cycle assessment was carried out. The system was modeled integrating all available knowledge, from pilot-scale data to process simulators, and following upscaling frameworks. PHA-based items outperform their commercial counterparts since they show environmental benefits thanks to the avoided electricity obtained in the cogeneration heat and power unit within the PHA production; however, these environmental benefits are very sensitive to the substituted electricity environmental burdens. The different levels of development and market implementation are seen as critical and therefore attention should be paid on how upscaling affects the results and interpretation of the LCA while serving as a guide for process and product development.

Energetic Assessment of SCWG Experiments with Reed Canary Grass and Ethanol Solution on Laboratory and Pilot Scale

The energetic assessment of biomass conversion processes is important for evaluating their application potential. Process energy efficiency is often evaluated based on simulation results for processes under idealized conditions. The present work uses data from a laboratory plant to perform an evaluation of the supercritical water gasification (SCWG). For this purpose, experiments were conducted with two types of feedstock, Reed Canary Grass and ethanol. Under the present lab-plant configuration, a cold gas efficiency of up to 79% could be reached, which is comparable to the gasification of biomass in fluidized-bed gasifiers or entrained-flow gasifiers. Based on the obtained data on the produced substances and their distribution, a scale up to pilot plant size was conducted. A model was derived from the available data to energetically assess different SCWG plant sizes based on real laboratory results. This model can be transferred to other feedstocks and other process designs to approximate the optimal size for the used biomass feedstock. The importance of heat recuperation in this process is described in detail based on pilot-scale data.

Ex-ante life cycle assessment of FineFuture flotation technology: case study of Grecian Magnesite

PurposeThis study aims at evaluating the environmental performance of a novel froth flotation technology in mining industry from a life cycle perspective. The technology is being developed under EU Horizon 2020 project titled “FineFuture” (FF) with the aim of saving valuable materials in fine particles that are currently wasted due to lack of technology.MethodsFF relies on chemically enhancing the physical characteristics of particles allowing it to float and concentrate.Prospective life cycle assessment (pLCA) was conducted for two possible industrial applications of FF flotation technology in the case study of Grecian Magnesite (GM) which is a main magnesium oxide producer in Europe. Each application can be perceived as a standalone comparative LCA study comparing current system with future system incorporating FF technology on industrial scale.Results and discussionThe future scenarios did not decisively support FF technology in neither of the two applications from an environmental point of view. When applied to fines of < 4 mm granular size with the aim of material recovery, the future scenario performed better than the current situation only in 5 out of 16 impact categories. The main issue is the added burden of calcination phase. When the technology was tested to upgrade the existing magnesite concentrate before calcination, it introduced some gains in most of the impact categories, but the difference compared to the current situation is not very considerable. Testing improved scenarios showed a great benefit to the overall performance of the scenarios by introducing cleaner fuels and burners in calcination phase.Conclusion and recommendationsOverall, the results tend to favour applying FF technology to upgrade low quality concentrates rather than beneficiating < 4 mm fines. However, and in any case, if FF technology is to be applied, combining it with cleaner fuels and burners in calcination should be prioritized. Furthermore, it was found that improving the purity (i.e. quality) in the flotation tank output is a key factor from an environmental view. The results also showed little impact of the added electric energy demand from the new units. As any pLCA, the study has limitations mainly originating from the low technology readiness level (TRL) when data collection activities were carried out. Further studies should start from pilot-scale data and adopting more accurate upscaling approaches to calculate the impacts of a full industrial deployment of the technology.

Removal of Mn (II) from aqueous solution via biosorption technology for a drinking water treatment plant: From laboratory-scale tests to semi-industrial scale predictions

Biosorbents have drawn increasing attention for removing pollutants on lab-scale systems. Although biosorbents are ecofriendly, their potential on large-scale columns has not been assessed yet. This study evaluated the potential of sugarcane bagasse (SB) for the removal of Mn (II) from aqueous media. Firstly, batch experiments were conducted to establish the equilibrium data and predominant adsorption mechanisms. Secondly, a mathematical model was proposed to predict the breakthrough curves in a fixed-bed column. Alternatively, lab- and pilot-scale removal experiments were performed to validate the accuracy of the proposed model. Once the model was validated on both scales, it was employed to predict breakthrough curves for a semi-industrial column under actual conditions of a drinking water treatment plant. Langmuir isotherm (qmax: 2.82 mg/g) and Langmuir kinetic model (R2>0.98 and x2<0.01) reproduced the experimental equilibrium and kinetic behavior. The mathematical model successfully replicated the lab- and pilot-scale breakthrough data (R2>0.95 and x2<0.1). Therefore, its prediction on the semi-industrial scale was reliable. However, short breakthrough and exhaustion times were obtained on this column, which hinders SB from applying in actual raw water. The described methodology can be employed to assess the potential of other biosorbents for industrial applications.

Life cycle energy use and greenhouse gas emissions for a novel algal-osmosis membrane system versus conventional advanced potable water reuse processes: Part I

This study applied a life cycle assessment (LCA) methodology for a comparative environmental analysis between an innovative algae resource recovery and near zero-liquid discharge potable reuse system (i.e., the main system) versus a conventional potable reuse system (i.e., the benchmark system) through energy use and greenhouse gas (GHG) emissions. The objective of this study is to demonstrate that pilot-scale data coupled with LCA would provide valuable information for system optimization, integration, and improvements for the design of environmentally sustainable full-scale systems. This study also provides decision-makers valuable information regarding the energy demand and environmental impact of this innovative main system compared to a typical tried-and-true system for potable water reuse. The main system consists of a novel algal-based wastewater treatment coupled with a dual forward osmosis and seawater reverse osmosis (Algal FO-SWRO) membranes system for potable water recovery and hydrothermal liquefaction (HTL) to recover biofuels and valuable nutrients from the harvested algal biomass. The benchmark system refers to the current industry standard technologies for potable water reuse and waste management including a secondary biological treatment, microfiltration (MF), brackish water reverse osmosis (BWRO), ultraviolet/advanced oxidation process (UV-AOP), and granular activated carbon (GAC), as well as anaerobic digestion for sludge treatment. Respective energy and GHG emissions of both systems were normalized and compared considering 1 m3 of water recovered. Based on an overall water recovery of 76% designed for the benchmark system, the energy consumption totaled 4.83 kWh/m3, and the system was estimated to generate 2.42 kg of CO2 equivalent/m3 with most of the emissions coming from the biological treatment. The main system, based on an overall water recovery of 88%, was estimated to consume 4.76 kWh/m3 and emit 1.49 kg of CO2 eq/m3. The main system has high environmental resilience and can recover bioenergy and nutrients from wastewater with zero waste disposal. With the application of energy recovery devices for the HTL and the SWRO, increase in water recovery of the FO membrane, and replacement of the SWRO membrane with BWRO, the main system provides an energy-competitive and environmentally positive alternative with an energy demand of 2.57 kWh/m3 and low GHG emissions of 0.94 kg CO2 eq/m3.

Spatio-temporal prediction of temperature in fluidized bed biomass gasifier using dynamic recurrent neural network method

• Long-short term memory model is proposed to predict temperatures in fluidized bed gasifier. • The proposed model is validated with pilot-scale plant data resolved in space and time. • Multi-step ahead temperature progressions of freeboard, fluidized bed and outlet gas, are predicted. • Gated recurrent unit and long short-term memory models are compared. In this study, a long short-term memory (LSTM) based dynamic recurrent neural network model is proposed for multi-step ahead temperature predictions in a pilot-scale fluidized bed biomass gasifier (FBG). The LSTM model predicts not only the temporal but also the spatial distribution of temperature by considering the temperature of each region of the FBG (fluidized bed, freeboard and outlet gas) as a separate target parameter. The proposed model is validated by comparing simulation data with experimental observations acquired during operation of the FBG. The validation results reveal that the proposed LSTM model is capable of accurately (MAE<6) predicting 1-min-ahead temperature of all the FBG regions. The LSTM model is further challenged for temperature predictions at farther future points (3 min and 5 min ahead) to test the prediction limits of the LSTM model. For 5 min ahead predictions, the proposed LSTM-based prediction model is also compared with other state-of-the-art dynamic neural network methods that include the standard recurrent neural network (S-RNN) and its advanced variant, the gated recurrent unit (GRU). The comparative findings for far future predictions show that LSTM has the highest accuracy, and also exhibit that GRU does not have universally faster convergence than LSTM.

The role of chloramines in enhancing soluble manganese removal by manganese contactors by W.R. Knocke, A.M. Weber, P.H. Hargette and E. Odom

Abstract Manganese (Mn) contactors have previously been shown to be quite effective in removing dissolved Mn from drinking water. To date, the contactor media have been regenerated by the application of free chlorine, either continuously in the contactor-applied water or via offline regeneration. This research study was the first to evaluate the potential role of chloramines for the online regeneration of MnOx(s)-coated contactor media. Testing initially at the bench scale and then subsequently over 6 months at the pilot scale demonstrated that chloramines were quite effective at promoting significant media regeneration. Pilot-scale data showed that the presence of chloramines addressed approximately 80% of the media regeneration requirements, allowing for the addition of free chlorine following media backwashing to address the remaining oxidant needs for media regeneration. Hydraulic loading rates of up to 39 m/hr (16 gpm/ft2) could be employed and still produce highly effective Mn removal. The means by which these research results can be applied to other water treatment situations are also discussed.

Prediction of molecular distribution and temperature profile of FCC process through molecular-level kinetic modeling

This work aims to build a molecular-level process model framework to simulate the fluid catalytic cracking (FCC) process. The process model consists of the riser, regenerator, and separation models. A complex molecular-level kinetic model, including 3,652 molecules and 8,202 reactions, was developed for the heavy oil FCC process. The kinetic model was combined with the riser model, and a set of systematic pilot-scale FCC data was used to tune the kinetic parameter. Afterwards, a two-zone and two-phase regenerator model was built. The regenerator was combined with the riser model, and the coupled modeling and process simulation for the riser-type FCC unit was developed. The results indicate that the calculated values of product yields and key bulk properties agree well with the experimental data. Moreover, the molecular distribution of the product and temperature profile were predicted.

Off-Gas System Scale-Up of HIsarna Iron-Making Process: A CFD-Based Approach

For all industrial applications, predicting system characteristics and behavior plays a vital role before constructing costly and complex multi-physic systems. Correct and reliable predictions become even more important once the aim is to go from small- to large-scale processes to establish an industrial demonstrations. In this study, a CFD-based scale-up of HIsarna off-gas system based on the Eulerian–Lagrangian approach is investigated and detailed step in scale-up procedure is discussed. A three-dimensional CFD model is developed and validated based on the available pilot scale data and used to design and scale up the post-combustion chamber (also known as reflux chamber). Detailed kinetics for volumetric and gas–solid reactions are incorporated in validated CFD model with a special attention to the wall boundary condition and modeling. The effect of reflux chamber geometry, oxygen injection ports, oxygen injection flowrate, isolation wall thickness, and inlet flue gas composition on different system characteristics such as heat loss through the wall, CO–H2–carbon mixture conversion, flue gas, and wall temperature are investigated. The aim of the scaled up geometry, like pilot scale, is to achieve full combustion of unwanted species inside the reflux chamber to assure zero emissions from the off-gas system. Compared to the pilot scale, the scaled up reflux chamber is capable of handling and removing higher amount of unwanted species coming from the main reactor and therefore lower CO–H2 and carbon particle emissions, mainly due to a larger size which provides larger volume and residence time for volumetric and gas–solid reaction to proceed.

Alleviating mass transfer limitations in industrial external-loop syngas-to-ethanol fermentation

Mass transfer limitations in syngas fermentation processes are mostly attributed to poor solubility of CO and H2 in water. Despite these assumed limitations, a syngas fermentation process has recently been commercialized. Using large-sale external-loop gas-lift reactors (EL-GLR), CO-rich off-gases are converted into ethanol, with high mass transfer performance (7–8.5 g.L-1.h−1). However, when applying established mass transfer correlations, a much poorer performance is predicted (0.3–2.7 g.L-1.h−1). We developed a CFD model, validated on pilot-scale data, to provide detailed insights on hydrodynamics and mass transfer in a large-scale EL-GLR. As produced ethanol could increase gas hold-up (+30%) and decrease the bubble diameter (≤2 mm) compared to air–water mixtures, we found with our model that a high volumetric mass transfer coefficient (650–750 h−1) and mass transfer capacity (7.5–8 g.L-1.h−1) for CO are feasible. Thus, the typical mass transfer limitations encountered in air–water systems can be alleviated in the syngas-to-ethanol fermentation process.

Nanoplastics removal during drinking water treatment: Laboratory- and pilot-scale experiments and modeling

Microplastics detected in potable water sources and tap water have led to concerns about the efficacy of current drinking water treatment processes to remove these contaminants. It is hypothesized that drinking water resources contain nanoplastics (NPs), but the detection of NPs is challenging. We, therefore, used palladium (Pd)-labeled NPs to investigate the behavior and removal of NPs during conventional drinking water treatment processes including ozonation, sand and activated carbon filtration. Ozone doses typically applied in drinking water treatment plants (DWTPs) hardly affect the NPs transport in the subsequent filtration systems. Amongst the different filtration media, NPs particles were most efficiently retained when aged (i.e. biofilm coated) sand was used with good agreements between laboratory and pilot scale systems. The removal of NPs through multiple filtration steps in a municipal full-scale DWTP was simulated using the MNMs software code. Removal efficiencies exceeding 3-log units were modeled for a combination of three consecutive filtration steps (rapid sand filtration, activated carbon filtration and slow sand filtration with 0.4-, 0.2- and 3.0-log-removal, respectively). According to the results from the model, the removal of NPs during slow sand filtration dominated the overall NPs removal which is also supported by the laboratory-scale and pilot-scale data. The results from this study can be used to estimate the NPs removal efficiency of typical DWTPs with similar water treatment chains.

Predicting the performance of spiral-wound membranes in pressure-retarded osmosis processes

A process simulator has been developed to model and predict the performance of spiral-wound membrane modules in pressure retarded osmosis processes. This has involved automation of generalized protocols for the numerical integration of the solvent and solute flux equations (in conjunction with a suitable electrolyte equation of state) along the surface area of a spiral-wound membrane leaf. Performance equations are solved for discrete area elements and the spiral-wound character of the module as a whole is realized through the programmed sequence in which discrete elements are evaluated. This arrangement allows for mirroring the parabolic flow pattern of the feed stream in the spiral-wound membrane leaf. The total permeation (and, by extension, power density) is thus calculated in a manner that accounts for the driving force profile consistent with flow patterns specific to spiral-wound membranes. This effective treatment of each discrete element as a flat-sheet membrane enables the transferability of membrane parameters characterized in standard, coupon-scale experiments to the simulation of spiral-wound modules. This transferability is illustrated through comparisons of model predictions with published pilot-scale PRO data.

Modeling PFAS Removal Using Granular Activated Carbon for Full-Scale System Design.

Per- and polyfluoroalkyl substances (PFAS) are increasingly of interest to drinking water utilities due to state regulations, the release of federal and state health advisories, and public concern. Pilot-scale data were fitted for 16 PFAS species and five commercial-activated carbons using an open-source pore and surface diffusion model that includes an automated parameter-fitting tool. The estimated model parameters are presented, and an uncertainty analysis was evaluated considering the expected temporal variability of influent concentrations. Expected treatment performance differed between two seasons in the pilot phase for the same carbon, which was not captured by modeled uncertainty. However, modeling results can support a utility's decision to choose activated carbon, and make design and operational decisions that can address changing water production rates and treatment goals. For the utility that undertook this pilot study and their desired treatment goals, granular activated carbon (GAC) was found to be an effective treatment technology for PFAS removal.

Unifying high-pressure grinding rolls models

• This paper presents a steady-state high-pressure grinding rolls model. • It consists of unified previously published works. • It is calibrated on 18 pilot scale data sets. This paper unifies high-pressure grinding rolls crusher (HPGR) models of the working gap, mass flow rate, power draw, and centre and edge product particle size distribution models to produce a framework that allows the simulation of the equipment for process optimization and control studies. Calculating the model parameters requires exclusively a series of HPGR tests, but the framework prediction capabilities remain confined within the range of the calibration data set. The model inputs the specific force, the roll speed, the zero-gap and the edge width setting. It makes no restricting hypothesis regarding slip or material bypass and does not necessitate prior knowledge of the flakes density. The work presents an extensive model analysis with calibrations on 18 data sets with invariable moisture content and feed particle size distribution, which includes computing the parameter uncertainties and analyzing the residuals. Seven of these data sets are new, while the rest consists of previously published values. The analysis shows that the model performs satisfactorily for all data sets.