Critical Process Variables Research Articles

Optimizing olefin purification: An artificial intelligence-based process-conscious PI controller tuning for double dividing wall column distillation

This study investigates the light olefin separation using dual dividing wall columns (DWCs) and compares various proportional-integral (PI) controller tuning methods. A novel process-conscious reinforcement learning (RL) approach, utilizing Deep Deterministic Policy Gradient (DDPG), is developed by incorporating domain-specific knowledge into the reward function to ensure compliance with critical process variables and product purity. The DDPG-based PI tuning is evaluated against traditional methods such as Aspen’s recommended initial tuning, Ziegler-Nichols (ZN), and Cohen-Coon (CC). The result shows that the DDPG method significantly enhances control stability and accuracy, reducing error by factors of 11.9, 2.3, and 1.6 compared to Aspen, ZN, and CC, respectively. Additionally, DDPG demonstrates superior energy efficiency, consuming 13% less energy than the next best method. It also reduces purification costs by up to 13.5% and CO2 emissions by 20% compared to other methods. This study highlights the potential of integrating advanced RL techniques into industrial process control, delivering substantial improvements in stability, energy efficiency, economic, and environmental performance.

Effect of peak temperature on microstructure and mechanical performances of ART-Flash processed medium Mn steel

The combination of ART and Flash annealing has been utilized to enhance the mechanical properties of medium Mn AHSS, demonstrating significant effectiveness. However, the industrial application was constrained by the absence of research on the impact of critical processing variables. In this work, the peak temperature of the flash heating was varied in the intercritical field to manipulate the microstructure and mechanical response of a 5 wt% Mn steel. The mechanisms of microstructural evolution during heat treatment and deformation were thoroughly discussed through interrupted tensile testing. A collection of ultrafine multiphase structures, consisting of retained austenite (RA), ferrite, and martensite with flexible phase constituents, were constructed, resulting in an ultrahigh YS increment (∼450 MPa) and exceptional strength-ductility combination (YS: 1125 MPa, UTS: 1544 MPa, TE: ∼21 %). The significant strengthening is primarily attributed to the increase in α′-martensite, while the sustained TRIP effect induced by stable retained austenite is responsible for the appropriate ductility. The interaction between austenite and adjacent phases and grain size exhibit a crucial influence on the mechanical stability of austenite. Benefiting from the “shielding” effect provided by the surrounding martensite, the Flash-810 sample achieved great ductility, despite possessing only a 5 % volume fraction of retained austenite. The YS of the experimental steels is sensitive to peak temperature, while the UTS and ductility remains relatively stable. Our work suggests the substantial potential of intercritical flash heating in enhancing the mechanical response of medium Mn steels and the profound significance of controlling the peak temperature.

Tensor-based statistical learning methods for diagnosing product quality defects in multistage manufacturing processes

Multistage manufacturing processes with identical stages provide three-dimensional process data in which the first dimension represents the process (control/sensing) variable, the second is the stage, and the third is the measurement/sampling/data acquisition time point. Diagnosing quality faults in such processes often requires the simultaneous identification of crucial process variables and stages associated with product quality anomalies. Most existing diagnosis methods convert 3D data into a 2D matrix, resulting in loss of information and reduced diagnostic accuracy and stability. To address this challenge, we propose a penalized tensor regression model that regresses the product quality index against its 3D process data. For the estimation of high-dimensional regression coefficients with the limited amount of historical data, we apply the CANDECOMP/PARAFAC and Tucker decompositions to the coefficient tensor, which significantly reduces the number of parameters to be estimated. Based on the decompositions, a new regularization term is designed to enable the joint identification of critical process variables and stages. To estimate the parameters, we develop the block coordinate proximal descent algorithm and provide its convergence guarantee. Numerical studies demonstrate that the proposed methods can enhance diagnostic stability and on average improve the diagnostic accuracy by around 20% over existing benchmarks.

Production of 5-fluorouracil-loaded PLGA nanoparticles with toroidal microfluidic system and optimization of process variables by design of experiments

In recent decades, microfluidics has presented new opportunities for the production of nanoparticles (NPs). However, to achieve rapid clinical translation, the production of PLGA NPs in a single microfluidic channel for both the pharmaceutical research and industry without the need for scaling is still limited. The aim of this study was to accomplish the production of reproducible and stable 5-FU loaded Poly(lactic-co-glycolic acid) (PLGA) NPs, using an innovative toroidal microfluidic system, for cancer therapy. The toroidal microfluidic system enabled the production of spherical NPs ranging from 100 to 150 nm by adjusting both the TFR within the range of 5–15 mL/min and FRR between 1:3 and 1:7. A systematic assessment of critical process variables (total flow rate; TFR, flow rate ratio; FRR) for the production of PLGA NPs was conducted using Design of Experiment (DoE). The NPs, which exhibit a uniform size distribution, remained stable even after centrifugation and storage for 3 months at 4 °C. The encapsulation efficiency of drug and the concentration of NPs were not affected by changing process parameters. The effective 5-FU encapsulation into NPs resulted in a controlled in vitro drug release. Due to the controlled release profile of the 5-FU loaded PLGA NPs, the formulation was a promising candidate for mitigating the toxic side effects of free 5-FU and improving cancer treatment. In conclusion, toroidal microfluidic system enables high-volume production of stable PLGA NPs, both with and without 5-FU.

Comparative assessment of simulation-based and surrogate-based approaches to flowsheet optimization using dimensionality reduction

This work proposes a framework for simulation-based and surrogate-based reduced space Bayesian optimization of process flowsheets. The framework uses global sensitivity analysis for dimensionality reduction via the identification of critical process variables that contribute significantly to the variability of the objective function (e.g. productivity and operating costs). Both simulation- and surrogate-based algorithms are applied to a biopharmaceutical and a chemical process simulator for the production of plasmid DNA and dimethyl ether (DME), respectively. Their capabilities are assessed in terms of the trade-off between computational effectiveness and solution accuracy. Results indicate that simulation-based Bayesian optimization achieves better objective function values, while surrogate-based Bayesian optimization is more computationally effective.

Adaptive soft sensor considering process state in film manufacturing process and identification of critical process variables

In the film manufacturing process, process variables, such as temperature and pressure, are measured and controlled to manage the film properties, such as thickness and optical characteristics. Each film property is regulated by the product specifications, and having all film properties adhere to the specifications is important. In this study, soft sensors were utilized to predict film properties and the importance of the variables that affect the film property variations for each soft sensor was calculated. Different process states were considered in the soft sensor modeling, a different soft sensor was constructed for each state, and soft sensor models with film property prediction accuracies higher than those of the conventional ones were achieved. The importance of the process variables was calculated by adding random variables to identify the truly important variables. The appropriate process variables were confirmed to be detected from the perspective of on-site process engineers. This study contributes to the application of soft sensors in film manufacturing processes.

Thermal and Mechanical Investigation of Friction Stir Welding with Disparate Materials AA6061 and AA7075

Background:: The primary objective of this study is to assess the impact of welding conditions on the mechanical properties of friction stir-welded butt joints created from two distinct aluminium alloys, namely, AA6061 and AA7075. Friction stir welding (FSW), known for its innovation and low-energy solid-state bonding technique, was employed in this research. Methods:: FSW experiments were carried out on both AA6061 and AA7075 alloys using a computer numerical control (CNC) machine. The selection and design of the tool geometry were meticulous, with an emphasis on new pin profiles that are nearly flat at the weld contact point. Precisely, four distinct tool geometries were machined from HC-HCr (High carbon, high chromium steel): Circular, Square, Tapered third, and Triangular. Critical process variables that significantly influence weld quality include rotation speed (800 rpm-1400 rpm) and traverse speed (12 to 25 mm/min). These variables were carefully optimized to achieve flawless welds. During the friction stir welding process, the nugget zone undergoes significant deformation, leading to the formation of a new microstructure that substantially impacts the mechanical properties of the joint. Results:: This study comprehensively investigates the thermal and mechanical properties of friction stir welding using aluminium alloys AA6061 and AA7075, considering various tool shapes. Among the four tool shapes employed, two were found to yield higher hardness values (referred to as BH). Notably, the square-shaped tool produced the highest temperature, reaching up to 690ºC, as determined by thermocouple readings. Based on the findings, the optimal FSW parameters for enhancing hardness involve an axial feed and spindle speed of 800 rpm combined with a feed rate of 15 mm/min. These parameters were identified as crucial for achieving the desired mechanical properties in the friction stir-welded joints. Conclusion:: This study presents new developments in FSW technology, which may have patent implications.

Characterisation of process-induced variability in wrinkle defects during double diaphragm forming of non-crimp fabric

Experiments and numerical simulations are employed to determine the critical process variables that affect the quality of a two-layer biaxial non-crimp fabric (NCF) preformed using the double diaphragm forming (DDF) process. To prevent wrinkling defects, the process variables must be controlled, including the vacuum pressure, diaphragm tension, and ply-tool alignment. Inter-ply friction plays a critical role in the deformation mechanism, while the frictional interaction between the diaphragm and tool has a limited impact. The study also reveals that geometrical features have an impact on the variability induced by the forming process. Preforms deformed to tools with geometrical features of higher asymmetry or Gaussian curvatures reduce the uncertainty of the forming process by maintaining the same preform quality despite changes in forming variables.

Process Optimization of Tinospora cordifolia Extract-Loaded Water in Oil Nanoemulsion Developed by Ultrasound-Assisted Homogenization.

Nanoemulsions are gaining interest in a variety of products as a means of integrating easily degradable bioactive compounds, preserving them from oxidation, and increasing their bioavailability. However, preparing stable emulsion compositions with the desired characteristics is a difficult task. The aim of this study was to encapsulate the Tinospora cordifolia aqueous extract (TCAE) into a water in oil (W/O) nanoemulsion and identify its critical process and formulation variables, like oil (27-29.4 mL), the surfactant concentration (0.6-3 mL), and sonication amplitude (40% to 100%), using response surface methodology (RSM). The responses of this formulation were studied with an analysis of the particle size (PS), free fatty acids (FFAs), and encapsulation efficiency (EE). In between, we have studied a fishbone diagram that was used to measure risk and preliminary research. The optimized condition for the formation of a stable nanoemulsion using quality by design was surfactant (2.43 mL), oil concentration (27.61 mL), and sonication amplitude (88.6%), providing a PS of 171.62 nm, FFA content of 0.86 meq/kg oil and viscosity of 0.597 Pa.s for the blank sample compared to the enriched TCAE nanoemulsion with a PS of 243.60 nm, FFA content of 0.27 meq/kg oil and viscosity of 0.22 Pa.s. The EE increases with increasing concentrations of TCAE, from 56.88% to 85.45%. The RSM response demonstrated that both composition variables had a considerable impact on the properties of the W/O nanoemulsion. Furthermore, after the storage time, the enriched TCAE nanoemulsion showed better stability over the blank nanoemulsion, specially the FFAs, and the blank increased from 0.142 to 1.22 meq/kg oil, while TCAE showed 0.266 to 0.82 meq/kg.

Fabrication of carotenoid enriched Crocus sativus w/o nanoemulsion: Optimization and pharmaceutical characterization

Herbal-based preparations have progressively attracted more interest nowadays due to multiple therapeutic approaches. Major concerns with the use of herbal preparations and their application as functional ingredients are about their stability, solubility, and bioaccessibility. Crocus sativus is one of the crucial nootropic traditional herbs, rich in potential phytochemicals used for the treatment of long-standing disorders. Many studies have suggested that the bioactive present in C. sativus extract may undergo enzymatic degradation in the intestine and hence less bioavailable at the site of action. A novel drug delivery approach to herbal preparation may combat the associated challenge. The present research focuses on the feasibility of developing w/o nanoemulsion incorporating the carotenoid-enriched C. sativus extract by spontaneous emulsification method. Critical process variables were optimized using 32 factorial design with Design of Expert software. The optimized formulation of w/o nanoemulsion comprising 9 mg/ml of C. sativus extracts with 12% internal phase, 21% S-mix, and 67% oil phase produced a globule size of 94.34 nm. The total content present in the nanoemulsion was 83% bioactive determined by the High Performance Liquid Chromatography- Photo Diode Array (HPLC-PDA) method. Further characterization, the Transmission Electron Microscopy (TEM) images showed the overall nanoemulsion system morphology with uniform distribution of globules of the internal phase. The in vitro release of bioactives from developed nanoemulsion showed an initial burst release of 60% at 1 hour following slow drug release. Results demonstrated that w/o nanoemulsion of C. sativus extract overcome the associated challenges and may reserve its space for medicinal application.

Artificial Intelligence for Hybrid Modeling in Fluid Catalytic Cracking (FCC)

This study reports a novel hybrid model for the prediction of six critical process variables of importance in an industrial-scale FCC (fluid catalytic cracking) riser reactor: vacuum gas oil (VGO) conversion, outlet riser temperature, light cycle oil (LCO), gasoline, light gases, and coke yields. The proposed model is developed via the integration of a computational particle-fluid dynamics (CPFD) methodology with artificial intelligence (AI). The adopted methodology solves the first principle model (FPM) equations numerically using the CPFD Barracuda Virtual Reactor 22.0® software. Based on 216 of these CPFD simulations, the performance of an industrial-scale FCC riser reactor unit was assessed using VGO catalytic cracking kinetics developed at CREC-UWO. The dataset obtained with CPFD is employed for the training and testing of a machine learning (ML) algorithm. This algorithm is based on a multiple output feedforward neural network (FNN) selected to allow one to establish correlations between the riser reactor feeding conditions and its outcoming parameters, with a 0.83 averaged regression coefficient and an overall RMSE of 1.93 being obtained. This research underscores the value of integrating CPFD simulations with ML to optimize industrial processes and enhance their predictive accuracy, offering significant advancements in FCC riser reactor unit operations.

Fermented formulation of Silybum marianum seeds: Optimization, heavy metal analysis, and hepatoprotective assessment

BackgroundFermented formulations are extensively used in Ayurveda due to several benefits like improved palatability, bioavailability, pharmacological potential, and shelf life. These formulations can also quench the heavy metals from the plant material and thus reduce the toxicity. Seeds of Silybum marianum (L.) Gaertn. are widely used for the management of many liver diseases. Study design and methodsIn the present study, we developed a novel fermented formulation of S. marianum seeds and evaluated parameters like safety (heavy metal analysis) and effectiveness (hepatoprotective). As the developed formulation's validation is crucial, the critical process variables (time, pH, and sugar concentration) are optimized for alcohol and silybin content using the Box-Behnken design (BBD). ResultsThe response surface methodology coupled with BBD predicted the optimized conditions (fermentation time (28 days), pH 5.6, and sugar concentration (22.04%)) for the development of a fermented formulation of the selected herb. Moreover, the alcohol content (6.5 ± 0.9%) and silybin concentration (26.1 ± 2.1%) were confirmed in optimized formulation by GC–MS and HPTLC analysis. The optimized formulation was also analyzed for heavy metals (Pb, As, Hg, and Cd); their concentration is significantly less than the decoction of herbs. Further, the comparative evaluation of the developed formulation with the marketed formulation also confirmed that the fermented formulation's silybin concentration and percentage release were significantly enhanced. In addition, the developed fermented formulation's percentage recovery of HepG2 cell lines after treatment with CCl4 was significantly improved compared with the marketed formulation. ConclusionIt can be summarized that the developed fermented formulation improves safety and effectiveness compared to other market formulations. Finally, it can be concluded that the developed fermented formulation could be further explored as a better alternative for developing Silybum marianum preparation.

Enhanced cellulose extraction from agave plant (Agave americana Species) for synthesis of magnetic/cellulose nanocomposite for defluoridation of water

Research on fluoride removal from water is currently focusing on the development of innovative materials for defluoridation water. The current study extracted and used enhanced cellulose from Agave americana species to synthesize a magnetic/cellulose nanocomposite for water defluoridation. Strong and light binary acids (H2SO4 and CH3COOH) were utilized to pretreat raw material to enhance cellulose extraction. Central composite design (CCD) was used to design experiments to find optimum condition for cellulose extraction. Four key factors (H2SO4 to CH3COOH ratio, acid concentration, temperature, and contact time) with three levels were designed by CCD. The lignin, hemicellulose and cellulose content of raw agave cellulosic fiber in this study were 5.5 ± 0.27, 20.5 ± 0.23 and 60.4 ± 0.31 % respectively. While, at the optimum conditions of 2.3 % acid concentration, 0.44 H2SO4 to CH3COOH ratio, 83.2 min of retention time, and 105.5 oC, cellulose content reached 940.25 %. Then, enhanced cellulose was covered with magnetic components, resulting in a magnetic/cellulose nanocomposite. The adsorbent materials were characterized by Fourier Transform Infrared (FTIR) spectroscopy, X-Ray Diffractometer (XRD), Scanning electron microscopy (SEM), and Dynamic light scattering (DLS). In addition, textural features of the adsorbent were investigated. All characterization results suggest that the synthesized adsorbent has the required properties. Adsorption tests were carried out using various interaction components. The combined effects of critical process variables on defluoridation were examined. The adsorption isotherm was calculated. Overall, the research demonstrated that the produced magnetic/cellulose nanocomposite can be employed as an efficient and ecologically acceptable adsorbent for fluoride removal from water.

Hamdard Institute of Pharmaceutical Sciences, Hamdard University, Pakistan

Tablet production is a very intricate process influenced by numerous process variables or parameters. This study aimed to identify the critical processing variables that affect Critical Quality Attributes (CQAs) of vitamin C film-coated caplets utilizing a statistical experimental design. A two-level complete factorial design with two central points was used to examine the process parameters that posed the greatest risk to CQAs. The process variables investigated included mesh size and duration for the lubrication process, as well as speed and main thickness for compression. Statistical results showed that mesh number, lubrication time, and their interaction significantly affect flow rate, Hausner ratio, and compressibility index. Higher mesh number and longer duration improved flow properties; lower main thickness significantly increased core caplet hardness; and lower dissolution rates were observed at higher compression speeds. Based on this study, it can be concluded that mesh number and lubrication time only significantly affected the bulk quality attributes but did not have a significant impact on the quality attributes of vitamin C caplets. On the other hand, the parameters of the compression process, such as speed and main thickness, greatly impacted the quality attributes of vitamin C caplets. In this study, the use of mesh number 20 with 7 minutes of lubrication, and a speed of 17 rpm with a main thickness scale of 2.00 were determined as the optimal process parameters. The optimal process parameters for the lubrication and compression processes were obtained from statistical analysis of the response data.

In the quest for ionic liquid entrainers for the recovery of R-32 and R-125 by extractive distillation under rate-based considerations

In line with the reduction targets imposed on the production of high global warming potential (GWP) hydrofluorocarbons by the Kigali Amendment to the Montreal Protocol, the recovery of refrigeration fluids at the end-of-life of refrigeration and air-conditioning equipment and the selective separation of the most valuable refrigerants is sought for recycling purposes and climate change mitigation. To that end, extractive distillation (ED) processes using ionic liquids (ILs) as entrainers is considered a promising technology to solve the difficulty of separating the typical close boiling or azeotropic behaviour of fluorinated hydrocarbon mixtures. This work provides insight into the design of ED processes evaluating the influence of both mass transfer phenomena (rate-based models) and IL properties (absorption capacity, solubility selectivity and viscosity) on the critical process variables (e.g., solvent-to-feed ratio, reboiler temperature, packing height) to separate the components of the binary mixture R-410A (50 wt% difluoromethane (R-32) + 50 wt% pentafluoroethane (R-125)) with minimum energy consumption and purity greater than 99.5 wt% of the two products. Results point to the solubility selectivity as the most influential IL property, and to [C2C1im][SCN], among all ILs assessed, as a promising entrainer because of its high R-32/R-125 solubility selectivity and low viscosity, which enables to operate the ED process at lower temperatures. The recovered R-32 can be used as a greener alternative to the high-GWP R-410A, as well as a main component in the formulation of new low-GWP mixtures.

Microwave-assisted Synthesis and Characterization of Acrylamide Grafted Copolymer of Okra Gum with the Application of 32 Central Composite Designs (CCD)

Introduction: Modified polymeric materials are nowadays most commonly used for making modified release pharmaceutical dosage forms. Methods: In this present research study, a grafted copolymer is synthesized by using acrylamide and okra gum with the help of the redox initiator method. The method was optimized using a 3-factor, 2-level central composite design (CCD). Optimization of the synthetic process was done by the application of QbD, amount of acrylamide (1-10g), amount of initiator (50-150 mg), and microwave irradiation exposure time (1-5 min) were taken critical process variables and response factors were selected as percentage grafting (% grafting) and percentage yield (% yield). Results and Conclusion: The optimized formulation (F13) having a maximum percentage of grafting 82.2 and a percentage yield of 12.54 of the formulation’s acrylamide (5.5g), amount of initiator (100 mg), and microwave irradiation exposure time (3 min). Characterization of synthesized grafted copolymer product was performed by using differential scanning calorimetry (DSC), FT-IR spectroscopy, and scanning electron microscopy (SEM).

Dynamic risk-based process design and operational optimization via multi-parametric programming

We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, as an explicit function of safety–critical process variables deviation from nominal operating conditions. Process design-aware risk-based multi-parametric model predictive control strategies are then derived which offer the advantages to: (i) integrate safety–critical variable bounds as path constraints, (ii) control risk based on multivariate process dynamics under disturbances, and (iii) provide model-based risk propagation trend forecast. A dynamic optimization problem is then formulated, the solution of which can yield optimal risk control actions, process design values, and/or real-time operating set points. The potential and effectiveness of the proposed approach to systematically account for interactions and trade-offs of multiple decision layers toward improving process safety and efficiency are showcased in a real-world example, the safety–critical control of a continuous stirred tank reactor at T2 Laboratories.

Development of a computational thermodynamics EERZ model for the improvement of hot rolled light steel profiles steel refining

Light steel profiles are steels with medium C, Mn and Si contents. In these steels internal cleanness is important for mechanical properties and surface quality, both during hot rolling and during the coating of the final product. Production costs of these steels are defined by the total heat time, from charging the Electric Arc Furnace (EAF) to starting continuous casting. Ladle furnace (LF) processing has a large influence on heat time and costs related to deoxidation, meeting composition requirements with a high yield of oxidizing alloying additions and proper rinsing to promote homogenization and cleanness. Thermodynamics and kinetics control how slag and steel oxidation levels evolve, as well as the evolution of the non-metallic inclusion population since at this stage primary oxides are the main inclusions. A model based on solid thermodynamic and kinetics fundamentals would be extremely useful for the producer of these steels, so that the evolution of the heat in the ladle furnace could be properly understood and, afterward, improve. In this work, we present the development of an “Effective Equilibrium Reaction Zone” (EERZ) for the LF processing of these steels, using a well-established thermodynamic database for metal and slags and a module of commercial computational thermodynamic software. We aim at demonstrating that these easily accessible and useable tools can be very effective to help in industrial process development. Thus, based on three selected industrial heats we adjust the kinetic parameters to describe the processes occurring for tapping from the EAF to releasing the heat for continuous casting. Volumetric mass transfer coefficients, heat transfer coefficients and other less critical variables are adjusted based on these heats and the model is then tested in seven further heats of the same steel grade. Results indicate that it is possible to achieve a good fit of the model to the industrial process and this helps identifying critical process variables to optimize the process schedule of these steels. Furthermore, we identify possible current limitations of the modeling strategy as suggested in the preliminary steps for the development of a EERZ model that may be coupled for on-line use, the support LF operators.

Advanced Process Control for Clinker Rotary Kiln and Grate Cooler

The cement industry includes energy-intensive processes, e.g., clinker rotary kilns and clinker grate coolers. Clinker is obtained through chemical and physical reactions in a rotary kiln from raw meal; these reactions also involve combustion processes. The grate cooler is located downstream of the clinker rotary kiln with the purpose of suitably cooling the clinker. The clinker is cooled through the action of multiple cold air fan units as it is transported within the grate cooler. The present work describes a project where Advanced Process Control techniques are applied to a clinker rotary kiln and a clinker grate cooler. Model Predictive Control was selected as the main control strategy. Linear models with delays are obtained through ad hoc plant experiments and suitably included in the controllers' formulation. A cooperation and coordination policy is introduced between the kiln and the cooler controllers. The main objectives of the controllers are to control the rotary kiln and grate cooler critical process variables while minimizing the fuel/coal specific consumption of the kiln and the electric energy consumption of the cold air fan units within the cooler. The overall control system was installed on the real plant, obtaining significant results in terms of service factor and control and energy-saving performances.

RAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.