Batch Manufacturing Processes Research Articles

Comparative economic analysis of batch vs. continuous manufacturing in catalytic heterogeneous processes: impact of catalyst activity maintenance and materials costs on total costs of manufacturing in the production of fine chemicals and pharmaceuticals

Abstract To evaluate the feasibility of transforming batch manufacturing processes in the fine chemicals and pharmaceutical industries to continuous synthesis, Capex, Opex and the total cost of manufacturing are estimated for production facilities for the hydrogenation of a nitro compound to its final amino counterpart. Two cases are evaluated: First, a high annual production dedicated plant, designed on the basis of processing 100,000 kg of the principal raw material per year, where raw materials cost, catalyst cost, and catalyst activity maintenance are varied over a broad range for typical industrial cases. Second, a “short campaign” model for a small volume production trial setup designed for the manufacture of only 100 kg of the final product, as a way to evaluate relevant industrial scenarios of scale-up and process development. A comparison is made between slurry batch, catalyst basket batch reactor and fixed bed continuous reactor manufacturing facilities. The hydrogenation of 2,4-dinitrotoluene was chosen as a probe reaction for the development of the manufacturing processes, with costs of the key raw material varying between $5 and $100 per kilogram, costs of catalyst varying between $100 and $1,500 per kilogram, and catalyst activity maintenances varying between 1,000 and 2,000,000 total turnovers before a change in catalyst load is necessary. For low catalyst activity maintenance, the total manufacturing costs for the fixed bed reactor process were always found to be higher than those of the two other alternatives. As catalyst activity maintenance increases, the manufacturing costs for the continuous alternative rapidly fall, reaching savings between 37 and 75% compared to the base batch reactor case, depending on the combination of costs of the key raw material and catalyst used. Graphical Abstract

Scheduling and Logistics Optimization for Batch Manufacturing Processes With Temperature Constraints and Alternative Thermal Devices

Scheduling and Logistics Optimization for Batch Manufacturing Processes With Temperature Constraints and Alternative Thermal Devices

Design and Characterization of a Continuous Melt Milling Process Tailoring Submicron Drug Particles

Solid crystalline suspensions (SCSs) containing submicron particles were introduced as a competitive solution to increase dissolution rates and the bioavailability of poorly water-soluble drugs. In an SCS, poorly water-soluble drug crystals are finely dispersed in a hydrophilic matrix. Lately, melt milling as an adapted wet milling process at elevated temperatures has been introduced as a suitable batch manufacturing process for such a formulation. In this work, the transfer from batch operation to a two-step continuous process is demonstrated to highlight the potential of this technology as an alternative to other dissolution-enhancing methods. In the first step, a powder mixture of a model drug (griseofulvin) and a carrier (xylitol) is fed to an extruder, where a uniform suspension is obtained. In the second step, the suspension is transferred to a custom-built annular gap mill, where comminution down to the submicron region takes place. The prototype’s design was based on batch grinding results and a narrow residence time distribution, intended to deliver large quantities of submicron particles in the SCS. The throughput of the mill was found to be limited by grinding media compression. By inclining the mill at an angle, the grinding media position was manipulated, such that compression was avoided. Different states of the grinding media in the grinding chamber were identified under surrogate conditions. This strategy allows the maintenance of an energy-optimized comminution without adaption of the associated process parameters, even at high throughputs. Using this new process, the production of an SCS with 80–90 % submicron particles in a single passthrough was demonstrated.

Measurement of stress characteristics in flexible multi-thin-film substrates using tomography common optical path interferometry

Flexible displays are lightweight, thin, flexible, and impact-resistant, making them a major trend in the display industry. However, their short lifespan is a significant challenge. Thus, improving process parameters and extending product life through advanced measurement technology is crucial. Therefore, this study proposes using tomography common optical path interferometry (TCOPI) to measure and analyze the tomography stress characteristics of multi-thin-film flexible substrates in the folding direction. For polyethylene terephthalate (PET) and polyimide (PI), three thicknesses of indium tin oxide (ITO) were coated on the PET, graphene/PET, and graphene/PI flexible film substrates, and an automatic sliding-folding test platform was used to fold each flexible film substrate. Moreover, this study used TCOPI to measure and analyze the change in residual stress and the sheet resistance of the samples before and after folding. Additionally, this study conducted inward and outward folding tests on graphene/PET/graphene and graphene/PI/graphene substrates to analyze tomographic stress and sheet resistance in the folding direction. After 11,000 folding tests, the graphene/PET/graphene sample exhibited smaller stress and sheet resistance change rates compared to the graphene/PI/graphene sample, as measured by TCOPI. Consequently, the graphene/PET/graphene sample demonstrated better resistance to folding stress in this batch manufacturing process. Specifically, for the graphene layer of the graphene/PET/graphene sample, the minimum absolute value of the stress change rate before and after folding reached 5%. At the same time, the minimum sheet resistance value change rate was 8%. Based on the tomography stress measurements, suggestions for fine-tuning process parameters are provided to improve the flexible film substrate’s folding endurance.

1,2,3-Triazole Synthesis: Development of Safe and Effective Batch and Continuous Manufacturing Processes

1,2,3-Triazole Synthesis: Development of Safe and Effective Batch and Continuous Manufacturing Processes

Predictive State Observer-Based Set-Point Learning Control for Batch Manufacturing Processes With Delay Response

A predictive state observer (PSO) based iterative learning control (ILC) scheme is proposed for industrial batch manufacturing processes with delay response suffering from nonrepetitive uncertainties and disturbances. Combining with a delay-free output predictor, a PSO based feedback control structure is firstly presented to improve set-point tracking and disturbance rejection performance against nonrepetitive process uncertainties and disturbances for the initial batch run. Then an ILC law is introduced to update the closed-loop system set-point in order to improve the tracking performance from batch to batch. A delay-independent sufficient condition is established to ensure the convergence of output tracking error along the batch-direction, based on double-dynamic analysis. Moreover, another delay-dependent sufficient condition is constructed by linear matrix inequality to assess robust stability of the proposed two-dimensional control system along both the time- and batch-directions. Finally, an illustrative example and a real application to the batch temperature regulation of a 4-litre crystallizer are shown to validate the effect and advantage of the proposed ILC scheme.

High-performance flexible organic field effect transistors with print-based nanowires

Polymer nanowire (NW) organic field-effect transistors (OFETs) integrated on highly aligned large-area flexible substrates are candidate structures for the development of high-performance flexible electronics. This work presents a universal technique, coaxial focused electrohydrodynamic jet (CFEJ) printing technology, to fabricate highly aligned 90-nm-diameter polymer arrays. This method allows for the preparation of uniformly shaped and precisely positioned nanowires directly on flexible substrates without transfer, thus ensuring their electrical properties. Using indacenodithiophene-co-benzothiadiazole (IDT-BT) and poly(9,9-dioctylfluorene-co-benzothiadiazole) (F8-BT) as example materials, 5 cm2 arrays were prepared with only minute size variations, which is extremely difficult to do using previously reported methods. According to 2D-GIXRD analysis, the molecules inside the nanowires mainly adopted face-on π-stacking crystallite arrangements. This is quite different from the mixed arrangement of thin films. Nanowire-based OFETs exhibited a high average hole mobility of 1.1 cm2 V−1 s−1 and good device uniformity, indicating the applicability of CFEJ printing as a potential batch manufacturing and integration process for high-performance, scalable polymer nanowire-based OFET circuits. This technique can be used to fabricate various polymer arrays, enabling the use of organic polymer semiconductors in large-area, high-performance electronic devices and providing a new path for the fabrication of flexible displays and wearable electronics in the future.

Anomaly Detection in Batch Manufacturing Processes Using Localized Reconstruction Errors From 1-D Convolutional AutoEncoders

Multivariate batch time-series data sets within Semiconductor manufacturing processes present a difficult environment for effective Anomaly Detection (AD). The challenge is amplified by the limited availability of ground truth labelled data. In scenarios where AD is possible, black box modelling approaches constrain model interpretability. These challenges obstruct the widespread adoption of Deep Learning solutions. The objective of the study is to demonstrate an AD approach which employs 1-Dimensional Convolutional AutoEncoders (1d-CAE) and Localised Reconstruction Error (LRE) to improve AD performance and interpretability. Using LRE to identify sensors and data that result in the anomaly, the explainability of the Deep Learning solution is enhanced. The Tennessee Eastman Process (TEP) and LAM 9600 Metal Etcher datasets have been utilised to validate the proposed framework. The results show that the proposed LRE approach outperforms global reconstruction errors for similar model architectures achieving an AUC of 1.00. The proposed unsupervised learning approach with AE and LRE improves model explainability which is expected to be beneficial for deployment in semiconductor manufacturing where interpretable and trustworthy results are critical for process engineering teams.

Quality Control Method of Key Processes in Multi Variety and Small Batch Manufacturing Process Based on NAD-EWMA Control Chart

Quality Control Method of Key Processes in Multi Variety and Small Batch Manufacturing Process Based on NAD-EWMA Control Chart

Comparison of process mass intensity (PMI) of continuous and batch manufacturing processes for biologics

Biologics encompasses a wide variety of therapeutics including monoclonal antibodies, fusion proteins, and enzymes, among others. The biologics market is growing at a rapid pace and different manufacturing processes, including continuous manufacturing processes, are being increasingly adopted. There is a strong drive to assess the sustainability of such processes. Here, we calculated the process mass intensity (PMI) of a continuous manufacturing process and compared it to the PMI of batch processes for monoclonal antibodies (mAbs). Results show that the PMI of continuous manufacturing process is comparable to that of batch processes. Sensitivity analysis was performed to assess the impact of different process strategies on the material usage efficiency of continuous processes. Although PMI is a useful benchmarking metric of sustainability, it does not account for factors such as energy consumption which is a key driver of sustainability for biologics manufacturing. Comparison of a higher PMI continuous process with a lower PMI batch process operating at the same bioreactor scale shows that since the productivity (in g of drug substance, DS) per unit time is multifold higher for the continuous process, the overall energy consumption per unit of DS produced might be lower leading to a more environmentally sustainable process. This study highlights some of these key aspects that would require additional metrics and models to be developed to assess the overall sustainability of biologics processes.

Population-Based Meta-Heuristic Algorithms for Integrated Batch Manufacturing and Delivery Scheduling Problem

This paper addresses an integrated scheduling problem of batch manufacturing and delivery processes with a single batch machine and direct-shipping trucks. In the manufacturing process, some jobs in the same family are simultaneously processed as a production batch in a single machine. The batch production time depends only on the family type assigned to the production batch and it is dynamically adjusted by batch deterioration and rate-modifying activities. Each job after the batch manufacturing is reassigned to delivery batches. In the delivery process, each delivery batch is directly shipped to the corresponding customer. The delivery time of delivery batches is determined by the distance between the manufacturing site and customer location. The total volume of jobs in each production or delivery batch must not exceed the machine or truck capacity. The objective function is to minimize the total tardiness of jobs delivered to customers with different due dates. To solve the problem, a mixed-integer linear programming model to find the optimal solution for small problem instances is formulated and meta-heuristic algorithms to find effective solutions for large problem instances are presented. Sensitivity analyses are conducted to find the effect of problem parameters on the manufacturing and delivery time.

Model-Free Adaptive Iterative Learning Control Method for the Czochralski Silicon Monocrystalline Batch Process

Model-based control methods do not produce satisfactory control results with the batch process control of Czochralski (CZ) silicon monocrystalline with complex nonlinearity, large delay, and time-varying dynamics. Therefore, this paper proposes a data-driven model-free adaptive iterative learning control method (MFAILC) to achieve precision control of the batch process. Firstly, to improve the accuracy of the data-driven model, a novel deep learning model for crystal growth process is established by combining a stacked autoencoder (SAE) and a long short-term memory network (LSTM) to extract the working condition information and the dynamic timing features in process data. Traditional model-based control methods are limited by the difficulties in modeling and by the unmodeled dynamics. So, to overcome this problem, the heater controller and the crystal puller controller are designed, based on the iterative dynamic linearization technology, to ensure that the silicon monocrystalline batch manufacturing process always maintains precision control. Also, the discrete-time extended state observer (ESO) is introduced to compensate for the influence of disturbances, to improve the robustness of the control system. Finally, the efficacy of the proposed method is verified by applying it for the predictive modeling and batch control of thermal field temperature and crystal diameter during crystal growth.

Rapid Synthesis and Sintering of Metals from Powders.

Powder to bulk processes, such as additive manufacturing and metal injection molding (MIM), have enabled great potential for complex metal designing and manufacturing. However, additive manufacturing process normally introduces a high residue stress and textures due to the locally intense temperature. MIM is an excellent batch manufacturing process; nevertheless, it is not suitable for rapid screening and development of new metal compositions and structures due to the slow sintering process. Herein, an ultrafast high‐temperature sintering (UHS) process is reported that enables the rapid synthesis and sintering of bulk metals/alloys and intermetallic compounds. In this process, elemental powders are mixed and pressed into pellets, followed by UHS sintering in just seconds at a temperature between 1000 and 3000 °C. Three representative compositions, including pure metals, intermetallics, and multielement alloys, are demonstrated with a broad range of melting points. The UHS process for metal sintering is nonmaterials specific, in addition to being extremely rapid, which make it suitable for materials discovery. Furthermore, the sintering method does not apply pressure to the samples, making it compatible with 3D printing and other additive manufacturing processes of complex structures. This rapid sintering technique will greatly facilitate the development and manufacturing of metals and alloys.

In Vitro-In Vivo Correlation (IVIVC) Population Modeling for the In Silico Bioequivalence of a Long-Acting Release Formulation of Progesterone.

Health authorities carefully evaluate any change in the batch manufacturing process of a drug before and after regulatory approval. In the absence of an adequate in vitro–in vivo correlation (Level A IVIVC), an in vivo bioequivalence (BE) study is frequently required, increasing the cost and time of drug development. This study focused on developing a Level A IVIVC for progesterone vaginal rings (PVRs), a dosage form designed for the continuous delivery in vivo. The pharmacokinetics (PK) of four batches of rings charged with 125, 375, 750 and 1500 mg of progesterone and characterized by different in vitro release rates were evaluated in two clinical studies. In vivo serum concentrations and in vitro release profiles were used to develop a population IVIVC progesterone ring (P-ring) model through a direct differential-equation-based method and a nonlinear-mixed-effect approach. The in vivo release, , was predicted from the in vitro profile through a nonlinear relationship. was used as the input of a compartmental PK model describing the in vivo serum concentration dynamics of progesterone. The proposed IVIVC P-ring model was able to correctly predict the in vivo concentration–time profiles of progesterone starting from the in vitro PVR release profiles. Its internal and external predictability was carefully evaluated considering the FDA acceptance criteria for IVIVC assessment of extended-release oral drugs. Obtained results justified the use of the in vitro release testing in lieu of clinical studies for the BE assessment of any new PVRs batches. Finally, the possible use of the developed population IVIVC model as a simulator of virtual BE trials was explored through a case study.

A new approach in the economic design of acceptance sampling plans based on process yield index and Taguchi loss function

Acceptance sampling plan is used for decision making in the field of products quality control. These plans provide the producer and the consumer with decision rules for accepting or rejecting the lot. In this paper, a new approach for the economic design of resubmitted and multiple dependent state (MDS) sampling plans is proposed based on the process yield index considering Taguchi loss function and the average number of sample size n, which minimizes the costs related to the yield index Spk. In the proposed model the loss of a batch is considered rather than a single product. Also, the loss of both producer and consumer based on the yield index is minimized simultaneously through solving an optimization model and the parameters of the proposed sampling plans are obtained. The performance of both proposed sampling plans is analyzed and compared in terms of average sample number and total expected loss. The results indicate that both proposed sampling plans show satisfactory performance. Moreover, the advantages of the proposed model over the existing resubmitted and MDS sampling plans are presented. Finally, the application of the proposed model and sampling plans is illustrated through a real case in batch manufacturing processes.

Current status, enablers and barriers of implementing cellular manufacturing system in sports industry through ISM

The sports goods manufacturing industry in India has gained phenomenal growth over the past five decades and now holds a prominent place in the Indian economy by providing massive employment and export revenue to the country. To enhance the quality and efficiency of production the industry is trying to implement new and efficient techniques in its manufacturing operations. Cellular manufacturing is a solution to the batch manufacturing process; hence reducing the lead-time with higher machine utilization, improved product quality, and enhanced customer satisfaction. In this work an attempt has been made to study the current status, enablers, and barriers in the implementation of cellular manufacturing principles in the sports industry-specific to Meerut and nearby regions. An ISM based structural modeling and MICMAC analysis of implementation enablers, and barriers of cellular manufacturing practices in sports industry in Meerut are also presented. Management vision and Government support are the major driving enablers while ‘Limited technological support, infrastructure and role model industry’, and ‘Lack of management support’ are among the major barriers. The implementation barriers are also arranged into five categories based on the factors related to their removal. The practical implications and future scope of the study are also proposed.

A New Double Exponentially Weighted Moving Average Run-to-Run Control Using a Disturbance-Accumulating Strategy for Mixed-Product Mode

Mixed-product production is common practice in semiconductor manufacturing and has attracted considerable academic attention recently. The main purpose of the mixed-product mode is to manufacture a wide variety of products in the same time period, to meet customer demand. The complex and changeable group of products is called “mixed-product production mode.” A specific tool used and a specific product manufactured are together called a “thread.” There is a need for “threaded” Run-to-Run (RtR) control in semiconductor manufacturing. This article develops a new product-based, disturbance-accumulating strategy in threaded RtR control by means of the double exponentially weighted moving average (dEWMA) technique. The main idea is to develop two disturbance-accumulating strategies; one for the intercept update of the same product between cycles and the other for the disturbance update in terms of the previous product between cycles, cope with the transient and transition effects, respectively. A comprehensive simulation study is conducted to compare the performance of the proposed product-based RtR controller with the existing controls specifically designed for the mixed-product mode. The proposed control has great promise as an extremely competitive threaded RtR controller in practice. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Run-to-run (RtR) control has been widely applied in batch manufacturing processes to reduce variation and has been identified as a turn-key technology for maintaining quality outputs in semiconductor manufacturing. The mixed-product production mode poses a great challenge of how to adjust process recipes between different products to alleviate initial recipe bias, process shifts, and patterned disturbances from run to run, product to product, and cycle to cycle. The proposed mixed-product RtR controller is developed based on a novel disturbance-accumulating, product-based strategy. This new controller is easy to implement in practice and has been shown to be effective in producing high-quality control outputs.

Mode-cloud data analytics based transfer learning for soft sensor of manufacturing industry with incremental learning ability

In modern manufacturing enterprises, quality-related soft sensors are important in production, especially for batch manufacturing processes. In practice, batch processes frequently produce new process modes due to various factors, such as changes in operating recipes, uncertainty in the external environment, and various product specifications. When this is the case, directly applying the historical model may lead to unexpected results. Moreover, it may be impractical to conduct enough trial runs and wait until sufficient batches are available before modeling for the new mode. Therefore, the problem of modeling for a new process mode with insufficient data brings challenges for the soft-sensor issue, which has rarely been addressed before. To solve the problem as mentioned above, a novel transfer and incremental soft-sensor scheme is developed for batch manufacturing processes with the support of multiple historical process modes, termed mode-cloud here. Using the proposed algorithm, the production data from the cloud of historical modes can be explored and utilized regarding their different phase-based relationships between process variables and product qualities. Thus, a new objective function is constructed to automatically identify and quantify the information buried in the vast ocean of historical data to determine the initial soft-sensor model for the new mode with limited data. Besides, with the constant increase of new samples, the initial soft-sensor model can incrementally update model parameters and release the workload of repetitive modeling. With both transfer modeling and incremental updating abilities, the proposed algorithm, which we call mode-cloud based transfer incremental learning (MTIL), can not only offer high adaptability and flexibility to accommodate a new process mode quickly, but also ensure the prediction accuracy. The MTIL based soft-sensor scheme is applied to the real injection molding process for the illustration purpose.

Preclinical Development of CTX120, an Allogeneic CAR-T Cell Targeting Bcma

Autologous CAR-T cells targeting BCMA have induced robust and durable responses in patients with relapsed/refractory multiple myeloma. However, autologous cell therapies face several challenges which will likely limit the number of patients that will have access to these therapies. These limitations include manufacturing failure rates, wait time and supply constraints in addition to other factors such as reimbursement. Allogeneic CAR-T cells can potentially overcome these access challenges, and may have several other advantages over autologous therapies. Allogeneic CAR-T cells are derived from robust healthy donor T cells through a batch manufacturing process, which may result in a highly consistent product with greater potency and enable better safety management. Here we show further development and preclinical data for CTX120, an allogeneic “off the shelf” CAR-T cell targeting BCMA. CTX120 is produced using the CRISPR/Cas9 system to eliminate TCR and MHC class I, coupled with specific insertion of the CAR at the TRAC locus. CTX120 shows consistent and high percent CAR expression from this controlled insertion and exhibits target-specific cytotoxicity and cytokine secretion in response to BCMA positive cell lines. CTX120 CAR-T cells retain their cytotoxic capacity over multiple in vitro re-challenges, demonstrating durable potency and lack of exhaustion. In mouse models of multiple myeloma, CTX120 showed typical CAR-T persistence and eliminated tumors completely, resulting in long-term survival as compared to untreated animals. These data support the ongoing development of CTX120 for treatment of patients with multiple myeloma and further demonstrate the potential for our CRISPR/Cas9 engineered allogeneic CAR-T platform to generate potent CAR-T cells targeting different tumor antigens. DisclosuresDar:CRISPR Therapeutics: Employment, Equity Ownership. Henderson:CRISPR Therapeutics: Employment, Equity Ownership. Padalia:CRISPR Therapeutics: Employment, Equity Ownership. Porras:CRISPR Therapeutics: Employment, Equity Ownership. Mu:CRISPR Therapeutics: Employment, Equity Ownership. Kyungah:CRISPR Therapeutics: Employment, Equity Ownership. Police:CRISPR Therapeutics: Employment, Equity Ownership. Kalaitzidis:CRISPR Therapeutics: Employment, Equity Ownership. Terrett:CRISPR Therapeutics: Employment, Equity Ownership. Sagert:CRISPR Therapeutics: Employment, Equity Ownership.

Development of near infrared spectroscopic calibration models for in-line determination of low drug concentration, bulk density, and relative specific void volume within a feed frame

This study describes the development of a near infrared (NIR) calibration model for real time determination of drug concentration, powder density, and porosity or relative specific void volume (RSVV) of 3.00%w/w acetaminophen blends within a feed frame. The NIR calibration model was developed from 1.50 to 4.50%w/w of acetaminophen, using a high variability of major excipients (from 12.92 to 81.95%w/w) which facilitates the prediction of powder density and RSVV based on near infrared calibration spectra. The model using second derivative as spectral preprocessing explained the changes related to acetaminophen concentration in the first latent variable. The second latent variable was related to changes in concentration of microcrystalline cellulose and lactose in the powder blends. NIR calibrations were also developed based on the bulk density and RSVV of the powder blends using the same design as the API model, due to the physical properties of the particles and their effects on the NIR spectra. The RSVV was predicted for the independent set blends with an RSEP(%) below 4% with a significantly low bias (0.04 cm3/g) from reference values of 1.33 to 1.58 cm3/g. The bulk density model also exhibited excellent predictions with RSEP(%) below 2.6% and significantly low bias (0.01 g/cm3) from reference values of 0.45 to 0.51 g/cm3. The excellent results obtained show the potential of near infrared spectroscopic measurements within the feed frame for a Process Analytical Technology method to control the critical properties such as tablet mass, hardness and dissolution in batch and continuous manufacturing processes.