Industrial Processes Research Articles

Explainable machine learning models for defects detection in industrial processes

Machine learning algorithms in non-linear pattern recognition for defect detection in manufacturing processes are increasingly prevalent in the context of Industry 4.0. This scenario enables factories to develop methodologies for monitoring and controlling the quality of their products, obtaining better operational efficiency indicators, and offering increasingly competitive products in the market. Explainable Artificial Intelligence can facilitate understanding how the models make decisions and assist in tracking anomaly points. This paper proposes an explainability framework for machine learning models in defect detection. This work applied the multinational industry’s approach to the tire manufacturing process. Models such as random forest, gradient boosting decision tree, light gradient boosting machine, logistic regression, support vector machine, and multi-layer perceptron were considered to evaluate the performance of tires in compliance with production standards. Statistical methodologies had to be adopted to deal with the small number of samples training the ML models. The random forest and logistic regression models achieved the best performances with an accuracy of 92% and 96%, respectively. The Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations methods were used to identify the relevant process variables. The approach presents the global and local interpretations of the results, allowing for a deeper understanding of how process variables discriminate the fault. The proposed framework determines reference values for each selected process variable for tire manufacturing in compliance with quality standards. This approach provides a relevant tool for quality control management in tire manufacturing industries.

Sensitive Detection of OCS Using Thermal Conversion Combined with Spectral Reconstruction Filtering Differential Optical Absorption Spectroscopy.

Carbonyl sulfide (OCS) is a toxic gas produced during industrial processes that poses risks to both human health and industrial equipment. Therefore, detecting OCS concentrations plays a crucial role in early hazard warning. This paper presents an online system for detecting OCS at the ppb level using thermal conversion and spectral reconstruction filtering differential optical absorption spectroscopy (SRF-DOAS). First, OCS, which is not suitable for DOAS due to its weak absorption characteristics, is completely transformed into SO2 with strong absorption characteristics under high-temperature conditions. Then, the spectral reconstruction filtering method (SRF) is proposed to eliminate the noise and interference. The core idea of the method is to arrange the spectrum according to the spectral intensity from small to large rather than wavelength, reconstructing the spectrum into a new spectrum with linear characteristics. The reconstructed spectrum can remove noise and interference by linear fitting and retain the characteristic of SO2 oscillation absorption. Next, we demonstrate the ability of the reconstructed spectral method to remove noise and interference by comparing the spectra of the inverse-reconstructed gas mixture and SO2. The relative deviation of 0.88% at 100 ppb and detection limit of 7.26 ppb*m for OCS were obtained using the SRF-DOAS method. Finally, the reliability of the system was confirmed by measurements of OCS concentrations in mixture gas of OCS and air, as well as in human exhaled breath.

A Zeolitic Octahedral Metal Oxide with Ultrahigh Porosity for High‐temperature and High‐humidity Alkyne/Alkene Separation

AbstractZeolitic octahedral metal oxide is a newly synthesized all‐inorganic zeolitic material and has been used for adsorption, separation, and catalysis. Herein, a new zeolitic octahedral metal oxide was synthesized and characterized. The porous framework was established through the assembly of [P2Mo13O50] clusters with PO4 linkers. Guest molecules occupied the framework, which could be removed through heat treatment, thereby opening the micropores. The pore characteristics were controlled by the cations within the micropore, enabling the adjustment of the interactions with alkynes and alkenes. This resulted in good separation performance of ethylene/acetylene and propylene/propyne even under high temperature and humidity conditions. The high stability of the material enabled the efficient recovery and reuse without discernible loss in the separation performance. Due to the relatively weak interaction between the adsorbed alkyne and the framework, the adsorbent facilitated the recovery of a highly pure alkyne. This feature enhances the practical applicability of the material in various industrial processes.

Preparation of Newly Polymer-Coated Microbial Pellets and Their Adsorption and Degradation Properties for Oil-Containing Wastewater.

Water is the lifeblood of everything on earth, nourishing and nurturing all forms of life, while also contributing to the development of civilization. However, with the rapid development of economic construction, especially the accelerated process of modern industrialization, the pollution of oily sewage is becoming increasingly serious, affecting the ecological balance and human health. The efficient elimination of pollutants in sewage is, therefore, particularly urgent. In this paper, a core-shell microbial reactor (MPFA@CNF-SA-AM) was fabricated by using nanocellulose and sodium alginate (SA) particles embedded with microorganisms as the core and lipophilic and hydrophobic fly ash as the outer shell layer. Compared with that of free microorganisms and cellulose and SA aerogel pellets loading with microorganisms (CNF-SA-AM), which has a degradation efficiency of 60.69 and 82.89%, respectively, the MPFA@CNF-SA-AM possesses a highest degradation efficiency of 90.60% within 240 h. So that this self-floating microbial reactor has selective adsorption properties to achieve oil-water separation in oily wastewater and high effective degradation of organic pollutants with low cost. The adsorption curves of MPFA@CNF-SA-AM for diesel and n-hexadecane were studied. The results showed that the adsorption follows the Freundlich model and is a multimolecular layer of physical adsorption. In addition, the degradation mechanism of diesel oil was studied by gas chromatography-mass spectrometry. The results showed that diesel oil was selectively adsorbed to the interior of MPFA@CNF-SA-AM, and it was degraded by enzymes in microorganisms into n-hexadecanol, n-hexadecaldehyde, and n-hexadecanoic acid in turn, and finally converted to water and carbon dioxide. Compared with existing oily wastewater treatment methods, this green and pollution-free dual-functional core-shell microbial reactor has the characteristics of easy preparation, high efficiency, flexibility, and large-scale degradation. It provides a new, effective green choice for oily wastewater purification and on-site oil spill accidents.

Preliminary Purification and Partial Characterization of a Functional Bacteriocin of Lacticaseibacillus paracasei Zhang and Mining for its Gene Cluster.

Bacteriocins produced by lactic acid bacteria (LAB) have good potential for use as food biopreservatives. Lacticaseibacillus paracasei Zhang (L. paracasei Zhang) is both a food use and a probiotic bacterium. This study aimed to purify and preliminary characterize the active antibacterial metabolite of L. paracasei Zhang. The cell-free supernatant of L. paracasei Zhang was collected and purified by ultrafiltration and gel filtration chromatography. The 1-3kDa active fraction could inhibit the growth of Staphylococcus aureus but not Escherichia coli. Further antibacterial activity assays revealed its capacity to suppress various foodborne and human opportunistic pathogens (including Staphylococcus aureus, Pseudomonas fluorescens, Pseudomonas aeruginosa, Listeria monocytogenes, and Bacillus cereus), but not fungi. The antibacterial activity showed good tolerance to heat (40 to 100°C), acid-base (pH 2-3 and pH 6-10), and digestions by a number of industrial and animal/human enzymes (such as trypsin, pepsin, α-amylase, and protease K, except papain); these desired properties make it a suitable biopreservative to be used in harsh and complex industrial production processes. The high papain sensitivity suggested a proteinaceous/peptide nature of the bioactivity. Moreover, our genomic data mining for bacteriocin through BAGEL4 revealed an area of interest encoding a complete set of putative genes required for bacteriocin production. In conclusion, our study showed that L. paracasei Zhang can produce extracellular functional antibacterial metabolite, likely a class II bacteriocin. Our preliminary extraction and characterization of the active metabolite demonstrated that it has good potential to be used as a biopreservative or an agent for suppressing gastrointestinal infections.

Sensitive Feature Selection for Industrial Flotation Process Soft Sensor Based on Multiswarm PSO With Collaborative Search

Sensitive Feature Selection for Industrial Flotation Process Soft Sensor Based on Multiswarm PSO With Collaborative Search

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

Assessment of particulate Zn and Pb sources in the Orne watershed (Northeast France) using geochemical tools.

The Orne River, a tributary of the Moselle River, was highly impacted by industrial activities for more than one century. Land use along the Orne River is highly contrasted, with local specificity from its source to its junction with the Moselle River. The intense industrial activity left behind tons of steelmaking wastes (SMW) on the land surface and within the Orne riverbed. To assess the sources of particulate Zn and Pb transported as suspended sediment in the Orne River, different sets of samples from likely Zn- and Pb-bearing particle sources within the Orne watershed were collected. Three sets of samples were taken from potential sources representing detrital, urban, and inherited industrial particles. Mineralogy, element contents, and Zn and Pb isotope compositions were obtained to characterize and reveal the fingerprint of each set of samples. Soil samples were collected on distinct geomorphological areas characterized by different soil types and land uses. They all display detrital minerals assigned to the geological background. Urban dusts and steelmaking residues display specific mineral phases (sulfates and iron oxides, respectively). Element compositions present strong discrepancies between the distinct sets of samples. SMWs are particularly enriched in Fe, Zn, and Pb. Concerning isotopic composition, SMWs exhibit δ66Zn values ranging from - 0.67 to 1.66‰. Urban samples display δ66Zn values between - 0.11 and 0.13‰, and soils present δ66Zn values between - 0.24 and 0.47‰. The 206Pb/204Pb ratio was estimated to range from 17.550 to 18.807 for soils, from 17.973 to 18.219 for urban samples, and from 18.313 to 18.826 for SMWs. For each of the three sets of samples (soils, urban, industrial), variations of geochemical fingerprint were observed. For soils, the relatively large variations of Zn and Pb isotopic compositions were attributed to distinct land use and the contribution of atmospheric deposition. For industrial samples, the variations were more intense and may be attributed either to distinct industrial processes in the production of pig iron or to distinct furnace-flume treatment modes. The three sets of samples (urban, industrial, and detrital) could be distinguished based on Zn and Pb contents and isotopes. Finally, this study not only highlighted the sources that released particulate Zn and Pb into the Orne River system, it also demonstrated that urban particles are well defined in terms of Zn and Pb isotopic signatures, and those isotopic signatures could be extrapolated to other case studies.

Statistical process monitoring creates a hemodynamic trajectory map after pediatric cardiac surgery: A case study of the arterial switch operation

AbstractPostoperative critical care management of congenital heart disease patients requires prompt intervention when the patient deviates significantly from clinician‐determined vital sign and hemodynamic goals. Current monitoring systems only allow for static thresholds to be set on individual variables, despite the expectations that these signals change as the patient recovers and that variables interact. To address this incongruency, we have employed statistical process monitoring (SPM) techniques originally developed to monitor batch industrial processes to monitor high‐frequency vital sign and hemodynamic data to establish multivariate trajectory maps for patients with d‐transposition of the great arteries following the arterial switch operation. In addition to providing multivariate trajectory maps, the multivariate control charts produced by the SPM framework allow for assessment of adherence to the desired trajectory at each time point as the data is collected. Control charts based on slow feature analysis were compared with those based on principal component analysis. Alarms generated by the multivariate control charts are discussed in the context of the available clinical documentation.

ENERGY MANAGEMENT SYSTEM (EMS) AS A TOOL IN LIMITING THE COAL TRACE IN INDUSTRY

Increasing energy efficiency will be crucial to achieving the climate goals laid out in European Uniondirectives. This is particularly true for industries whose share of heat and energy consumption,with Poland as an example, is about one-third of the total. This challenge entails implicationsboth for the reduction of greenhouse gas emissions, especially CO2, and for maintaining thecompetitiveness of EU industry on the global market. This article presents the basic principlesand application of an Energy Management System – EMS – in industrial processes, together withthe monitoring of Key Energy Performance Indicators – KPIs – as a tool for making informedinvestment decisions to improve the energy efficiency of companies and industrial processes. Anattempt is made to present the situation in Poland in terms of the energy situation, with a focus onthe automotive industry as an example.

Enhanced monitoring and dynamic analysis of a concentrated solar thermal experimental plant for steam production

Solar heat for industrial processes (SHIP) is an increasingly interest option as a key strategy to decarbonise the industrial sector. Linear Fresnel Collectors are a promising technology due to their cost-effectiveness and efficiency. As part of the SOLPINVAP project, an experimental facility was constructed to prove the use of modular Linear Fresnel collectors for steam production in industrial processes. SHIP plants commonly use standard industrial sensors, which can result in high uncertainty when measuring absorbed and produced heat. This can be problematic when selling energy to customers through an energy purchase agreement. To address this issue, an uncertainty analysis was conducted in the experimental plant, and more precise instrumentation was added to the installation. This reduced the uncertainty in the absorbed energy in the solar field from 12 % to 3 % and obtaining an uncertainty of 3 % in the generated energy. Additionally, the operation of the plant was studied through experimental tests conducted during the summer of 2022, and the system dynamics were analysed during the period of peak heat absorption.

Impact of Protein Corona Formation and Polystyrene NanoparticleFunctionalisationon the Interaction with Dynamic Biomimetic Membranes Comprising of Integrin.

Plastics, omnipresent in the environment, have become a global concern due to their durability and limited biodegradability, especially in the form of microparticles and nanoparticles. Polystyrene (PS), a key plastic type, is susceptible to fragmentation and surface alterations induced by environmental factors or industrial processes. With widespread human exposure through pollution and diverse industrial applications, understanding the physiological impact of PS, particularly in nanoparticle form (PS-NPs), is crucial. This study focuses on the interaction of PS-NPs with model blood proteins, emphasising the formation of a protein corona, and explores the subsequent contact with platelet membrane mimetics using experimental and theoretical approaches. The investigation involves αIIbβ3-expressing cells and biomimetic membranes, enabling real-time and label-free nanoscale precision. By employing quartz-crystal microbalance with dissipation monitoring studies, the concentration-dependent cytotoxic effects of differently functionalised ~210 nm PS-NPs on HEK293 cells overexpressing αIIbβ3 are evaluated in detail. The study unveils insights into the molecular details of PS-NP interaction with supported lipid bilayers, demonstrating that a protein corona formed in the presence of exemplary blood proteins offers protection against membrane damage, mitigating PS-NP cytotoxicity.

A Unified Thermo-Fluid–Solid Formulation for FSI and Phase Change Problems Based on the Particle Finite Element Method

The modeling of industrial processes that involve phase change (e.g., casting, welding or additive manufacturing) is challenging due to their multiscale and multiphysics nature. The coupling of the fluid and solid mechanics is difficult due to the unknown and evolving fluid–solid interface. To deal with the difficulty of the coupling, a unified approach has been developed where fluid and solid regions are represented in the same computational domain and solved by a single solver. The interaction of fluid and solid regions is therefore automatically captured and the material can locally undergo phase transition. This allows to capture the flow in the melt and the thermal stresses in the solid. The solid material model is currently limited to linear elasticity, but it opens the path to more complex material models with plasticity modeling, which already exist in the literature. The proposed method is based on the Particle Finite Element Method (PFEM), which has been shown to accurately handle both fluid and solid mechanics. In this work, we present the key aspects of this novel unified formulation and the treatment of the fluid–solid interface in the PFEM context. The methodology is presented and verified using a set of tests. A laser spot welding example test case demonstrates the potential of combining the unified formulation with the interface treatment and the phase change capabilities and is used for the validation of the present technique.

Multi-step carbon emissions forecasting model for industrial process based on a new strategy and machine learning methods

The rising industrial sector capacity increases carbon emissions, necessitating a low-carbon transformation for global sustainability. Accurate multi-step forecasting of industrial carbon emissions is crucial for optimizing production, reducing energy consumption, improving efficiency, and achieving decarbonization. However, current forecasting faces challenges like model uncertainty and low accuracy with multi forecasting steps. To address these issues, this study proposed a multi-step carbon emissions forecasting model for industrial processes. Firstly, a new multi-step forecasting strategy framework was proposed. Secondly, SHapley Additive exPlanations (SHAP) and lagged autocorrelation analysis were used to extract key features affecting industrial carbon emissions. On this basis, the grey model, autoregressive moving average (ARMA) model, and least squares support vector machine (LSSVM) method were used to build forecasting models separately. The weights of the three models were solved using the induced ordered weighted average operator and the Monte Carlo search tree (MCST) method. The final forecasting results were obtained by using the weighted summation method. The data collected from the real industry were used to validate the proposed model. The results showed that the proposed model could accurately forecast industrial carbon emissions within 12 steps and its MAPE is less than 10%. The proposed multi-step industrial carbon emissions forecasting model can provide data support for industrial energy saving, carbon reduction, and green and sustainable development.

Machine learning-based study on the mechanical properties and embankment settlement prediction model of nickel–iron slag modified soil

Nickel–iron slag, a byproduct of industrial processes in China with an annual production exceeding 400,000 tons, is considered an industrial waste material. This study focuses on the rational utilization of nickel–iron slag by investigating its mechanical properties and road performance as a roadbed fill material. Initially, a detailed analysis of the grading curve of pure nickel–iron slag was conducted, leading to the proposal of various modification schemes for nickel–iron slag. Subsequently, static triaxial tests were performed on nickel–iron slag-clay mixtures to explore the impact of different factors on the stress–strain curve of nickel–iron slag-modified soil. Utilizing these discoveries, a formula for the molar Coulomb shear strength of nickel–iron slag-modified soil was derived. In addition, a numerical simulation study of a nickel–iron slag-reinforced embankment was conducted, integrating field tests. This aimed to investigate the variations in the compression layer sedimentation-thickness ratio and settlement factor of nickel–iron slag-modified soil reinforced embankment under different filling heights and slope rates. The results informed the development of a prediction model for the settlement ratio of nickel–iron slag-modified soil-reinforced embankment. Key findings indicate that pure nickel–iron slag exhibits poorly graded gravel sand characteristics, and optimal gradation is achieved when clay doping ranges from 30% to 40%. As the clay content increases, the stress–strain curve of nickel–iron slag-clay transitions from strain-hardening to strain-softening. Furthermore, the stress–strain curve of nickel–iron slag-cement-clay exhibits strain-softening, and the shear strength fitting formula demonstrates high computational accuracy with a small error range. Numerical simulations reveal that the sink-thickness ratio and settlement factor are minimally affected by the slope rate. The sink-thickness ratio increases with the elevation of filling height, while the settlement factor fluctuates within a small range. The proposed sink-thickness ratio prediction model exhibits high accuracy and strong generalization capabilities. This comprehensive study provides valuable insights into the efficient utilization of nickel–iron slag in construction and road engineering.

Unlocking Innovations: Exploring the Role of Kefir in Product Development

Abstract Purpose of Review The current review explores kefir's role in product development, investigating production methods, microbial composition, and health benefits across traditional and industrial processes. Variations in fermentation techniques and microbial diversity, highlighting milk and water kefir's potential as functional foods catering to diverse consumer preferences, have been evaluated. Recent Findings Recent literature underscores the diverse microbial community in kefir grains, which contributes to texture, flavor, and biological properties, including antimicrobial, anticarcinogenic, anti-inflammatory, and antioxidant effects. Kefir-based products are diverse and include traditional fermented milk drinks and innovative formulations like kefir cheese and powdered starter cultures, with emerging techniques such as spray drying resolving issues in shelf life and storage stability. Summary The beneficial aspect of Kefir makes it a promising avenue for innovation in product development by exploiting fermentation matrices and augmenting it with vitamins, minerals, and probiotics thereby enhancing its functional properties for gut health and diverse diet. Emerging technologies have been critical in tackling the challenges of shelf life and packaging costs. Continuing research and innovation addresses kefir's potential as a functional food, catering to the growing demand for health-oriented and sustainable products.

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.

Economic modelling and optimisation of SPRT-based quality control schemes for individual observations

In many industrial processes, it may not be practical to obtain sample sizes larger than one due to various reasons. As a result, practitioners must rely on control charts based on individual observations for statistical process monitoring and control. The sequential probability ratio test (SPRT) chart is an appropriate solution for such situations. Existing literature predominantly delves into the statistical aspects of designing or optimising an SPRT chart, neglecting the economic and associated design aspects. However, past experience suggests that statistical design may not always be optimal from an economic perspective. This paper aims to bridge this research gap by proposing a two-stage Markov chain approach to develop an economic model for the optimal design of the SPRT chart with individual observations. Following the principles of economic-statistical designs, optimal parameters are determined by solving a mixed-integer non-linear programming problem that minimises the long-run cost per item while ensuring compliance with practical constraints. Through an extensive study, we find that the economic performance of the SPRT chart surpasses that of competing individual control charts in many cases. This indicates that the economic-statistical design of the SPRT chart can yield significant economic benefits while maintaining desirable statistical properties. Moreover, enhancements have been introduced to refine the optimal design of the SPRT chart, retaining excellent overall performance across a wide range of shifts. Finally, we provide two industrial examples to illustrate the effectiveness of the proposed SPRT chart.

Tailoring the Structure-Property Relationship of Ring-Opened Metathesis Copolymers for CO2-Selective Membranes.

In this work, we explore the use of ring-opening metathesis polymerization (ROMP) facilitated by a second-generation Grubbs catalyst (G2) for the development of advanced polymer membranes aimed at CO2 separation. By employing a novel copolymer blend incorporating 4,4'-oxidianiline (ODA), 1,6-hexanediamine (HDA), 1-adamantylamine (AA), and 3,6,9-trioxaundecylamine (TA), along with a CO2-selective poly(ethylene glycol)/poly(propylene glycol) copolymer (Jeffamine2003) and polydimethylsiloxane (PDMS) units, we have synthesized membranes under ambient conditions with exceptional CO2 separation capabilities. The strategic inclusion of PDMS, up to a 20% composition within the PEG/PPG matrix, has resulted in copolymer membranes that not only surpass the 2008 upper limit for CO2/N2 separation but also meet the commercial targets for CO2/H2 separation. Comprehensive analysis reveals that these membranes adhere to the mixing rule and exhibit percolation behavior across the entire range of compositions (0-100%), maintaining robust antiplasticization performance even under pressures up to 20 atm. Our findings underscore the potential of ROMP in creating precisely engineered membranes for efficient CO2 separation, paving the way for their application in large-scale environmental and industrial processes.

From laboratory to industrial use: Understanding process variation during enzymatic protein hydrolysis with dry film fourier-transform infrared spectroscopy

Industries are continuously looking for dedicated sensor solutions for evaluating the chemical composition of products that can provide valuable insights into process control, process optimization, and product quality. A rapid and robust analytical method, Fourier-Transform Infrared (FTIR) spectroscopy, holds significant potential in this regard as it provides detailed compositional values of food nutrients. The main aim of the present study was to use dry film FTIR spectroscopy as an analytical tool to characterize products from an industrial enzymatic protein hydrolysis process and link this to understand industrial process variations. For this purpose, 463 protein hydrolysate samples were obtained from an industrial enzymatic protein hydrolysis plant. In the same period, systematic variations in process parameters such as raw material composition, enzyme type, and water addition, were performed. All samples were analyzed using dry film FTIR spectroscopy. Two subsets containing 200 and 68 hydrolysate samples were chosen for measuring average molecular weight (AMW) and collagen content respectively, providing reference data for constructing calibration models based on partial least squares regression (PLSR). The percentage of low molecular weight constituents was derived from the molecular weight distribution of size exclusion chromatograms of protein hydrolysates and also used in the modeling. Calibration models for the prediction of AMW, low molecular weight constituents, and collagen content were obtained with a good fit and lower estimation errors. Principal component analysis (PCA) of protein hydrolysates' FTIR spectra effectively differentiated process variations (enzyme types and raw materials) without extensive reference analysis. One-factor analysis of variance (ANOVA) tests was used to observe the impact of process variation on product quality. FTIR proved to be a sensitive method for liquid protein analysis and process control with a significant potential in process optimization approaches. To the authors’ knowledge, this is the first time dry film FTIR spectroscopy has been used to evaluate an industrial bioprocess with designed process variations.