Process Control Strategies Research Articles

Modelling of Coupled Mass and Thermal Balances in Hall-Heroult Cells During Anode Change

As the most routine work practice in an aluminum reduction cell, anode change introduces substantial perturbations that adversely affect the mass and heat balance of the cell which may lead to loss of process efficiency and increased energy consumption. The literature lacks a dynamic mathematical model that describes the interactions among cell variables (e.g., electrolyte temperature and flow, anode current distribution, and cell heat loss) during and following this operation, which could otherwise be used to understand the process in greater depth and to develop changes that improve process operations and control. This paper presents a spatially-discretised dynamic model for anode replacement that integrates mass balance, thermal balance and cell voltage to describe and predict local cell variables. It was experimentally validated with an industrial cell undergoing an anode change procedure. This generic model can be applied to different cell design or process conditions by using appropriate parameters (e.g., heat transfer coefficients, conductivities, and flow patterns). The model can be used to improve existing process operations or control strategies for higher process efficiency, lower energy consumption, and lower emissions.

Real-time monitoring and diagnosis scheme for IoT-enabled devices using multivariate SPC techniques

This article is aimed at condition monitoring and fault identification for Internet of Things (IoT) devices, and proposes a multivariate statistical process control scheme. The new method aims to detect sparse mean shifts using spatial rank and an improved adaptive elastic net algorithm, which can monitor the high-dimension data stream collected by IoT devices and pinpoint faulty variables. The new method is also applicable in the presence of a non-normal distribution and insufficient reference samples. Numerical simulations verify that the proposed method has clear advantages over existing methods. The case of wind turbines shows that the method can be applied to real-time monitoring and diagnosis of real IoT devices, which could provide valuable diagnosis of root cause and optimize subsequent maintenance strategies.

Proton-transfer-reaction mass spectrometry (PTR-MS) for online monitoring of glucose depletion and cell concentrations in HEK 293 gene therapy processes

ObjectivesThe applicability of proton-transfer-reaction mass spectrometry (PTR-MS) as a versatile online monitoring tool to increase consistency and robustness for recombinant adeno-associated virus (rAAV) producing HEK 293 bioprocesses was evaluated. We present a structured workflow to extract process relevant information from PTR-MS data.ResultsReproducibility of volatile organic compound (VOC) measurements was demonstrated with spiking experiments and the process data sets used for applicability evaluation consisted of HEK 293 cell culture triplicates with and without transfection. The developed data workflow enabled the identification of six VOCs, of which two were used to develop a soft sensor providing better real-time estimates than the conventional capacitance sensor. Acetaldehyde, another VOC, provides online process information about glucose depletion that can directly be used for process control purposes.ConclusionsThe potential of PTR-MS for HEK 293 cell culture monitoring has been shown. VOC data derived information can be used to develop soft sensors and to directly set up new process control strategies.

Modeling the time\xa0course of ComX: towards molecular process control for Bacillus wild-type cultivations

Wild-type cultivations are of invaluable relevance for industrial biotechnology when it comes to the agricultural or food sector. Here, genetic engineering is hardly applicable due to legal barriers and consumer’s demand for GMO-free products. An important pillar for wild-type cultivations displays the genus Bacillus. One of the challenges for Bacillus cultivations is the global ComX-dependent quorum sensing system. Here, molecular process control can serve as a tool to optimize the production process without genetic engineering. To realize this approach, quantitative knowledge of the mechanism is essential, which, however, is often available only to a limited extent. The presented work provides a case study based on the production of cyclic lipopeptide surfactin, whose expression is in dependence of ComX, using natural producer B. subtilis DSM 10 T. First, a surfactin reference process with 40 g/L of glucose was performed as batch fermentation in a pilot scale bioreactor system to gain novel insights into kinetic behavior of ComX in relation to surfactin production. Interestingly, the specific surfactin productivity did not increase linearly with ComX activity. The data were then used to derive a mathematic model for the time course of ComX in dependence of existing biomass, biomass growth as well as a putative ComX-specific protease. The newly adapted model was validated and transferred to other batch fermentations, employing 20 and 60 g/L glucose. The applied approach can serve as a model system for molecular process control strategies, which can thus be extended to other quorum sensing dependent wild-type cultivations.

Approaches to deep learning based manipulating strategy reconstructions for complex chemical processes

Modern complex chemical process operations produce a large amount of process monitoring and control time series data, which are potentially useful to extract valuable operating experiences and manipulating rules to improve the intelligence of process operations. Previous researches show that time series clustering is an effective method to mine historical control sequences. However, the actual working conditions often deviate from the historical data, making it difficult to reconstruct accurate process regulation manipulating strategies. In response to this problem, this paper proposes a manipulation strategies reconstruction method using a convolutional neural network model (MSR-CNN). Therein, the time series are hierarchically clustered according to the Levenshtein distance to obtain different classes of process disturbance states, and the corresponding manipulated sequences fragments obtained are treated with a symbolic aggregation approximation. Then the improved convolutional neural network is used for deep learning and manipulation strategies reconstruction of the process disturbance states and the corresponding manipulating sequences strings. Finally, applying to a numerical example and an ethanol–water distillation tower unit, the proposed method is proved to be effective. Compared with traditional supervisory control methods, our contributions can help well construct complex chemical process control strategies less dependent on human operators’ operating experiences.

Process control strategies for solar-powered carbon capture under transient solar conditions

This paper presents process control strategies commanding the novel solar stripper (So-St) network to effectively regenerate the rich solvent and ultimately replace the conventional desorption unit in the PCC. In this work, the effect of solar heat flux (SHF) is analyzed and categorized by the active control strategy in full synergy with the absorber operation. The mismatch in operation between the two ends of the solvent cycle (absorption vs. desorption) is buffered/regulated by solvent storage tanks. Three original and two combined control scenarios are developed and assessed based on their main controlled output and manipulated input variables. It is found that the lean loading control strategy provides the most consistent absorber operation, reliable CO2 productivity, and desirable thermodynamic regime. This superior performance of lean loading control stems from absorbing a relatively larger SHF range without violating solvent temperature and pressure-drop constraints in the So-St network. In practical applications, it is recommended that lean loading control should incorporate temperature control to monitor solvent temperature and provide appropriate remedy control actions when required. Such process control strategies can lead to effective operation of the “solar-powered” carbon capture (SP-PCC), allowing SP-PCC technology to be installed and operated independently and autonomously from the power plant, thus cutting the capture energy penalty by integrating renewable solar heat.

Characterization of cell cycle and apoptosis in Chinese hamster ovary cell culture using flow cytometry for bioprocess monitoring

Chinese hamster ovary (CHO) cells are by far the most important mammalian cell lines used for producing antibodies and other therapeutic proteins. It is critical to fully understand their physiological conditions during a bioprocess in order to achieve the highest productivity and the desired product quality. Flow cytometry technology possesses unique advantages for measuring multiple cellular attributes for a given cell and examining changes in cell culture heterogeneity over time that can be used as metrics for enhanced process understanding and control strategy. Flow cytometry-based assays were utilized to examine the progression of cell cycle and apoptosis in three case studies using different antibody-producing CHO cell lines in both fed-batch and perfusion bioprocesses. In our case studies, we found that G0/G1 phase distribution and early apoptosis accumulation responded to subtle changes in culture conditions, such as pH shifting or momentary glucose depletion. In a perfusion process, flow cytometry provided an insightful understanding of the cell physiological status under a hypothermic condition. More importantly, these changes in cell cycle and apoptosis were not detected by a routine trypan blue exclusion-based cell counting and viability measurement. In summary, integration of flow cytometry into bioprocesses as a process analytical technology tool can be beneficial for establishing optimum process conditions and process control.

Engineering Calculation And Algorithm Of Adaptation Of Parameters Of A Neuro-Fuzzy Controller

As part of the study, a control scheme with the adaptation of the coefficients of the neuron-fuzzy regulator implemented. The area difference method used as a training method for the network. It improved by adding a rule base, which allows choosing the optimal learning rate for individual neurons of the neural network. The neural network controller applied as a superstructure of the PID controller in the process control scheme. The dynamic object can function in different modes. This technological process operates in different modes in terms of loading and temperature setpoints. Because of experiments, the power consumption and the amount of time required maintaining the same absorption process, using a conventional PID controller and a neural-network controller evaluated. It concluded that the neuro-fuzzy controller with a superstructure reduced the transient time by 19%.

Development of a Digital Twin for Enzymatic Hydrolysis Processes

Enzymatic hydrolysis processes can be used to produce organic nutrient media from renewable raw materials. However, many of these processes are not optimally designed, so expensive enzymes and substrates are wasted. Mathematical models and Digital Twins (DTs) are powerful tools, which can be used to optimize bioprocesses and, thus, increase the yield of the desired products. Individual enzymatic hydrolysis processes have already been modeled, but models for the combined starch hydrolysis and proteolysis, or DTs, are not available yet. Therefore, an easily adaptable, dynamic, and mechanistic mathematical model representing the kinetics of the enzymatic hydrolysis process of the combined starch hydrolysis and proteolysis was developed and parameterized using experimental data. The model can simulate the starch hydrolysis process with an agreement of over 90% and the proteolysis process with an agreement of over 85%. Subsequently, this model was implemented into an existing DT of a 20 L stirred tank reactor (STR). Since the DT cannot only map the kinetics of the enzymatic process, but also the STR with the associated periphery (pumps, heating jacket, etc.), it is ideally suited for future process control strategy development and thus for the optimization of enzymatic hydrolysis processes.

Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid

The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit.

Hydrogen production and temperature control for DME autothermal reforming process

This study focuses on the control of dimethyl ether (DME) autothermal reforming (ATR) process integrated with proton exchange membrane fuel cells to provide adequate external power for an on-board reformer hydrogen vehicle. ATR reaction was made over Pd–Zn/γ-Al2O3 catalyst, which provides higher selectivity to the hydrogen due to its good oxidation and reforming activities. Catalyst activity was tested experimentally by varying the temperature. The hydrogen volume fraction is close to 45% at the optimal temperature of 400 °C. The objective of the process control scheme was to control the ATR reactor's temperature and the hydrogen production rate. A combination of the feedforward control and the closed-loop control (based on PID control) was applied wherein the DME, and air flow rates were adjusted automatically, and steam was supplied excessively to promote reactions. Due to its good oxidation and reforming activity, it had a high selectivity to hydrogen. The experimental results showed that the volume fraction of hydrogen in the hydrogen-rich gas is close to 45% at the optimum temperature of 400 °C. The control strategy could ensure that the hydrogen production rate changed with the change of fuel cell load and did not affect the temperature of the autothermal reformer. In this way, the stable operation of the reformer was realized, and the fuel cell had a sufficient supply of hydrogen.

Energy efficient design of bio-butanol purification process from acetone butanol ethanol fermentation

Butanol has the potential as a transportation fuel since its energy density is similar to gasoline and higher than ethanol. The Acetone-Butanol-Ethanol (ABE) fermentation is the current main method to produce bio-butanol where the fermentation should be operated in diluted surroundings to avoid toxicity to microorganisms. This article aims to develop efficient separation sequences to purify individual components from the ABE fermentation. The base design comes from the reference proposed by Patraşcu et al. [3]. With an updated thermodynamic model, several novel design alternatives are proposed and investigated to reduce energy consumption in the separation sequences. Therein, the proposal of removing acetone at first, and purifying ethanol via integration of distillation and membrace not only reduces total energy consumption and total annual cost by 57% and 52%, but also reaches targeting purity of 99.5% for butanol, acetone and ethanol. Heat integration and column stacking can reduce the overall energy consumption furthermore. This study also carries on plantwide control for the processes with heat integration and column stacking. The superior control performance with ±10% changes in throughput and feed compositions is validated for the proposed plantwide process control scheme.

Grey-box model calibration and validation for a continuous horizontal fluidized bed dryer

Fully continuous drying can improve the production flexibility and throughput of tablets manufactured by wet granulation lines. A recently developed prototype incorporates a screw conveyor to a horizontal fluidized bed dryer for pharmaceutical particles. Research on advanced process control strategies led to the publication of a two-phase tanks-in-series framework to describe its dynamics. This paper calibrates the model to replicate the prototype behavior. A grey-box identification methodology estimates the empirical and uncertain parameters using experimental input-output data. The underlying assumptions are cross-validated with time-domain comparisons, output root mean square errors and confidence intervals. Results show that the model reproduces the observed trends in general and the discrepancies result from mechanical issues on the equipment.

Bioreactor control systems in the biopharmaceutical industry: a critical perspective.

Industrial-scale bioprocessing underpins much of the production of pharmaceuticals, nutraceuticals, food, and beverage processing industries of the modern world. The profitability of these processes increasingly leverages the economies of scale and scope that are critically dependent on the product yields, titers, and productivity. Most of the processes are controlled using classical control approaches and represent over 90% of the industrial controls used in bioprocessing industries. However, with the advances in the production processes, especially in the biopharmaceutical and nutraceutical industries, monitoring and control of bioprocesses such as fermentations with GMO organisms, and downstream processing has become increasingly complex and the inadequacies of the classical and some of the modern control systems techniques is becoming apparent. Therefore, with increasing research complexity, nonlinearity, and digitization in process, there has been a critical need for advanced process control that is more effective, and easier process intensification and product yield (both by quality and quantity) can be achieved. In this review, industrial aspects of a process and automation along with various commercial control strategies have been extensively discussed to give an insight into the future prospects of industrial development and possible new strategies for process control and automation with a special focus on the biopharmaceutical industry.Supplementary InformationThe online version contains supplementary material available at 10.1007/s43393-021-00048-6.

Machine learning for intrusion detection in industrial control systems: challenges and lessons from experimental evaluation

Gradual increase in the number of successful attacks against Industrial Control Systems (ICS) has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process anomalies. Towards this end, a class of anomaly detectors, created using data-centric approaches, are gaining attention. Using machine learning algorithms such approaches can automatically learn the process dynamics and control strategies deployed in an ICS. The use of these approaches leads to relatively easier and faster creation of anomaly detectors compared to the use of design-centric approaches that are based on plant physics and design. Despite the advantages, there exist significant challenges and implementation issues in the creation and deployment of detectors generated using machine learning for city-scale plants. In this work, we enumerate and discuss such challenges. Also presented is a series of lessons learned in our attempt to meet these challenges in an operational plant.

Activity-Based Cell Sorting Reveals Resistance of Functionally Degenerate Nitrospira during a Press Disturbance in Nitrifying Activated Sludge.

ABSTRACTManaging and engineering activated sludge wastewater treatment microbiomes for low-energy nitrogen removal requires process control strategies to stop the oxidation of ammonium at nitrite. Our ability to out-select nitrite-oxidizing bacteria (NOB) from activated sludge is challenged by their metabolic and physiological diversity, warranting measurements of their in situ physiology and activity under selective growth pressures. Here, we examined the stability of nitrite oxidation in activated sludge during a press disturbance induced by treating a portion of return activated sludge with a sidestream flow containing free ammonia (FA) at 200 mg NH3-N/liter. The nitrite accumulation ratio peaked at 42% by day 40 in the experimental bioreactor with the press disturbance, while it did not increase in the control bioreactor. A subsequent decrease in nitrite accumulation within the experimental bioreactor coincided with shifts in dominant Nitrospira 16S rRNA amplicon sequence variants (ASVs). We applied bioorthogonal noncanonical amino acid tagging (BONCAT) coupled with fluorescence-activated cell sorting (FACS) to investigate changes in the translational activity of NOB populations throughout batch exposure to FA. BONCAT-FACS confirmed that the single Nitrospira ASV washed out of the experimental bioreactor had reduced translational activity following exposure to FA, whereas the two Nitrospira ASVs that emerged after process acclimation were not impacted by FA. Thus, the coexistence of functionally degenerate and physiologically resistant Nitrospira populations provided resilience to the nitrite-oxidizing function during the press disturbance. These results highlight how BONCAT-FACS can resolve ecological niche differentiation within activated sludge and inform strategies to engineer and control microbiome function.IMPORTANCE Nitrogen removal from activated sludge wastewater treatment systems is an energy-intensive process due to the large aeration requirement for nitrification. This energy footprint could be minimized with engineering control strategies that wash out nitrite-oxidizing bacteria (NOB) to limit oxygen demands. However, NOB populations can have a high degree of physiological diversity, and it is currently difficult to decipher the behavior of individual taxa during applied selective pressures. Here, we utilized a new substrate analog probing approach to measure the activity of NOB at the cellular translational level in the face of a press disturbance applied to the activated sludge process. Substrate analog probing corroborated the time series reactor sampling, showing that coexisting and functionally degenerate Nitrospira populations provided resilience to the nitrite oxidation process. Taken together, these results highlight how substrate analog approaches can illuminate in situ ecophysiologies within shared niches, and can inform strategies to improve microbiome engineering and management.

Quadratic function based price adjustment strategy on monitoring process of power consumption load in smart grid

Based on the data collected from smart meters, electricity pricing models can be developed to balance power supply and demand in time slot and obtain the optimal consumption loads and prices. However, in real life, users’ reserved consumption requirement loads sometimes deviate significantly from the optimal consumption loads obtained from models, which results in overloaded power systems or even power cuts. To address this issue, an engineering process control strategy has been proposed in this paper to minimize the difference between the optimal and the users’ reserved consumption requirement loads. We proposed an exponential weighted moving average model to predict the load difference in future time slots, and also developed a novel quadratic function based demand response mechanism to adjust the power price for power providers. The demand response mechanism can be used to adjust the price in the future time slots when the predicted demand exceeds the upper or lower boundary. Simulation results indicate that the quadratic function adjustment strategy has excellent performance in a practical power market in Singapore. Compared with the linear function based adjustment method, the proposed quadratic function based adjustment method decreases the adjustment times and standard errors of residuals, and increases the social welfare and power suppliers’ profits under the same boundary conditions. In addition, the performance of the proposed strategy demonstrated its competency in peak-cutting and valley-filling and balancing energy provision with demands.

JCAR017 Is a Defined Composition CAR T Cell Product with Product and Process Controls That Deliver Precise Doses of CD4 and CD8 CAR T Cell to Patients with NHL

Background:JCAR017 is a CD19-directed 4-1BB CAR T cell product with a highly controlled manufacturing process that enables administration in a defined composition at a precise dose of CD8 and CD4 CAR T cells. Preliminary safety data on JCAR017 in relapsed/refractory (R/R) B-cell NHL (TRANSCEND NHL 001, NCT02631044; interim results previously reported [Abramson, 14-ICML 2017]) showed lower rates of cytokine release syndrome (CRS) and neurotoxicity (NT) than reported for other CD19-directed CAR T cell products with broad heterogeneity of CAR T cell dosing and cell composition characteristics. The manufacturing process control strategy for JCAR017 was designed to minimize between-lot variability in drug product quality attributes.Methods:JCAR017 was manufactured from non-Hodgkin lymphoma (NHL) patient-derived autologous T cells in TRANSCEND NHL 001. JCAR017 drug product attributes were analyzed for phenotypic, functional, and cell health related attributes. The memory and cell health phenotypes of CAR T cells were examined using flow cytometry and T cell functionality was assessed using in vitro antigen-specific bioassays.Results:JCAR017 process and product control begins with an automated wash and T cell purification that results in pure CD8+ (median 99%, Inter Quartile Range [IQR] 98-100%) and CD4+ (median 99%, IQR 99-100%) populations (data presented for patient lots manufactured through 01AUG2017). Separate CD4+ and CD8+ T cell populations are transduced with lentivirus to express a CD19-directed CAR with a 4-1BB/CD3ζ endodomain. The CD4+ and CD8+ T cell fractions are expanded separately to a target CAR T cell dose and formulated / cryopreserved separately. Cultures are optimized for the unique biology of each phenotype. To minimize between-lot variance in cell health, the drug product is held at constant volume with a tight range of viable cell concentrations (CD8+: median 31x106 cells/mL, IQR 28-40 x106 cells/mL, N=38; CD4+: median 35x106 cells/mL, IQR 31-40x106, N=36). Additionally, JCAR017 cell health is assessed by measuring post-thaw cell surface Annexin V and active intracellular Caspase 3. The median % Annexin V+ is 11%, IQR 9-18% (N=33) of CD8+CAR+ T cells and 10%, IQR 8-17% (N=31) of CD4+CAR+ T cells. Caspase 3 expression is similar to Annexin V. In vitro antigen-specific cytokine accumulation and intracellular cytokine bioassays show similar low variance between lots for multiple cytokines. For example, after CD19 stimulation TNFα production has a tight range with relative standard deviation (RSD) of 37% for CD4+CAR+ T cells (N=59) and 51% for CD8+CAR+ T cells (N=61). Details will be presented and compared to a similar CAR T cell product without a defined composition.JCAR017 is administered in precise quantities of CD4+CAR+ and CD8+CAR+ T cells to patients. In an ongoing, multicenter, Phase 1 trial (TRANSCEND NHL 001), JCAR017 is administered as a flat dose at dose level 1 (DL1) of 5×107 CAR+ T cells (2.5x107 CD4+CAR+ T cells and 2.5x107 CD8+CAR+ T cells) or at dose level 2 (DL2) 10×107 CAR+ T cells (5x107 CD4+CAR+ T cells and 5x107 CD8+CAR+ T cells). At the site, JCAR017 is thawed and a patient-specific volume is withdrawn and administered. The range of administered dose was found to have low variability: 2.4-2.7x107 (target ±8%) CD4+CAR+ or CD8+CAR+ T cells at DL1 (N=48) and 4.6-5.1x107 (target ±8%) CD4+CAR+ or CD8+CAR+ T cells at DL2 (N=20). After administration to the patient, JCAR017 has a slow expansion profile with peak expansion at approximately 15 days with low rates of severe CRS and NT and an emerging dose:response relationship. Full PK, clinical efficacy, and safety of JCAR017 will be presented separately.Conclusions:The process and product control of JCAR017 results in high T cell purity and low between-lot variance. Three aspects of JCAR017 manufacturing contribute to the low variability between lots and encouraging clinical profile: 1) a precise, consistent flat dose of administered CD4+ and CD8+ CAR T cells; 2) control and optimization of CD4 and CD8 T cell culture conditions that results in low variability of cytokine production (e.g. IL-2, TNFα and IFNγ, etc.); and 3) constant formulation and volume of drug product that contributes to consistent cell health. The process control strategy and defined cellular composition of JCAR017 could contribute to the lower rates of CRS and NT than reported for other CD19-targeted CAR T cell products in clinical development for NHL. DisclosuresRamsborg:Juno Therapeutics: Employment, Equity Ownership, Patents & Royalties. Guptill:Juno Therapeutics: Employment, Equity Ownership. Weber:Juno Therapeutics: Employment, Equity Ownership. Christin:Juno Therapeutics: Employment, Equity Ownership. Larson:Juno Therapeutics: Employment, Equity Ownership, Patents & Royalties. Lewis:Juno Therapeutics: Employment, Equity Ownership. Mallaney:Juno Therapeutics: Employment, Equity Ownership. Bowen:Juno Therapeutics: Employment, Equity Ownership. Higham:Juno Therapeutics: Equity Ownership. Albertson:Juno Therapeutics: Employment, Equity Ownership, Patents & Royalties.

On-line characterization of wood chip brightness and chemical composition by means of visible and near-infrared spectroscopy

Abstract Historically, on-line and real-time measurement of wood chip properties in the pulp and paper industry has been a challenge and has hampered the development of advanced process control strategies. In this study, visible and near-infrared (VIS-NIR) spectroscopy is investigated as a means to characterize wood chip brightness and chemical composition (i.e. extractives, lignin and holocellulose content) on-line. The estimated standard error on the holocellulose reference measurement was significantly reduced using data reconciliation. VIS-NIR calibration models were developed using partial least square regression. Derivative and baseline correction were found to be the most appropriate pre-processing methods. Model desensitization to the influence of moisture content and temperature by means of external parameter orthogonalization resulted in more robust models critical for on-line applications under harsh industrial conditions. Wavelength selection improved model accuracy for all properties. A comparison of two different spectrometer and probe combinations demonstrated that, after wavelengths selection, a non-contact measurement of wood chips performs as well as a contact measurement of wood powder for monitoring chemical composition. On-line prediction of wood chip brightness and chemical composition using the developed VIS-NIR models was demonstrated over 7 months in a kraft pulp mill processing both hardwood and softwood chips.

Data-driven process control for manufacturing spiral bevel and hypoid gears by using design for six sigma (DFSS) considering numerical loaded tooth contact analysis (NLTCA)

Data-driven process control considering both geometric and loaded contact performance evaluations has been an increasingly important stage in field of spiral bevel and hypoid gears. A new data-driven manufacturing process control strategy is proposed for a high performance spiral bevel and hypoid gears. Here, to distinguish with the conventional simulated loaded tooth contact analysis (SLTCA) using economical finite element software package, the numerical loaded tooth contact analysis (NLTCA) is of more flexibility and practicality. In light of the advantages of the improved design for six sigma (DFSS), it is integrated with NLTCA for establishing a novel data-driven process control of gear manufacturing. Firstly, in improved DFSS framework, quality function deployment (QFD) is used to determine four sub-objective high-performance evaluation items. Then, their data-driven relationships between machine settings are respectively determined by using NLTCA. In particular, the manufacturing process control is further converted into multi-objective optimization (MOO) modification of the hypoid generator settings. Finally, an interactive preference point approach is applied for data-driven control of its iterative step and it can obtain a robust solution from Pareto optimal front. A case study is provided to verify the proposed methodology.