Flushing operations in multiproduct lubricant oil pipelines are a critical determinant of product purity, operational efficiency, and economic performance, yet traditional approaches often rely on operator experience and trial-and-error, leading to excessive waste and frequent cross-contamination. This research presents an integrated framework combining data-driven analysis, experimental validation, and process optimization to address these challenges and optimize packaging operations in petroleum industries. Industrial data capturing both successful and failed flushes were analyzed using a suite of candidate machine learning (ML) classification models including ensemble methods, kernel-based models, and deep learning architectures to accurately classify flushing outcomes and identify the key factors driving successful flush. To mitigate the significant class imbalance between successful and unsuccessful flushes, Synthetic Minority Oversampling Technique (SMOTE) was employed, ensuring robust model performance. SHAP analysis revealed that flushing success is governed by complex, nonlinear interactions among lubricant type, viscosity contrasts, pipeline flow dynamics, flushing volume, and ambient temperature, emphasizing that no single factor dominates the process. Insights from this analysis informed the design of a bench-scale experimental rig that replicated industrial pipeline geometry and hydrodynamics, enabling systematic evaluation of improved drainage procedures, air-assisted flushing, and flow behavior. These results guided the development of a novel standardized and optimized flushing protocol, which, when implemented in a production line, reduced the failure rate from 11% to 3.6%, minimized flush volumes, and delivered significant cost savings. This study highlights the synergy between data-driven insights, experimental validation, and process optimization in achieving high-impact outcomes that are resilient, scalable, and sustainable. The framework not only enhances operational reliability in petroleum packaging operations but also provides a generalizable methodology applicable across process industries seeking to improve contamination control, efficiency, and economic performance.

Life Cycle Assessment (LCA) is a method used to evaluate the environmental impacts of materials and processes throughout their entire life cycle, from production to end-of-life. However, performing LCA at the early design stage of a chemical process is often challenging because Life Cycle Inventory (LCI) data for new or emerging chemicals are not readily available. To enable impact assessment under these data-limited conditions, this study employs Machine Learning (ML) and Scaling Index Regression models to estimate environmental impacts across all life cycle stages. Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost) are employed to develop models to predict Life Cycle Impact Assessment (LCIA) endpoint metrics such as Human Health Impact (HHI), Ecosystem Quality Impact (EQI), Global Warming Potential (GWP), and Resource Utilization Impact (RUI) during the production phase of a chemical based on thermodynamic and molecular descriptor properties of the chemicals. Regression models are then applied to estimate the impact of technologies used during the use phase and end-of life phase by determining emission factors for the different technologies involved in the process. To demonstrate the accuracy of the proposed framework a case study is presented to validate the model’s performance.

Biomaterials mimicking natural extracellular matrix are necessary to create an optimal microenvironment for cell adhesion, migration, proliferation, and differentiation. These scaffolds must possess bicontinuous interconnected porosity to ensure the effective exchange of oxygen, nutrients, and metabolic waste, which are crucial for developing functional tissues. Here, a novel bicontinuous interfacially jammed emulsion (BIJEL)-Integrated PORous Engineered System (BIPORES) is developed to confer bioinert synthetic polyethylene glycol diacrylate (PEGDA) with unique bicontinuous interconnected porosity and surface topography. This platform is fabricated through controlled phase separation and interfacial stabilization of two continuous phases by nanoparticles. Functional validation using human mesenchymal stem cells, and human induced pluripotent stem cells-derived cardiomyocytes and cardiac fibroblasts, reveals outstanding cell attachment, growth, proliferation, and/or differentiation within tissue-scale BIPORES scaffolds. These findings indicate that bicontinuous interconnected porosity with negative Gaussian curvature in the BIPORES scaffolds plays a key role in organ-scale tissue engineering and regeneration.

Our program focuses on exposing undergraduate students to graduate-level research with industrial applications. By collaborating with industries, we ensure that the research and development that the students are focused on is relevant to the real world and provides value. Here we present a case study of one of our projects which focused on the lubricant manufacturing industry. Lubricant manufacturing and processing facilities produce a variety of products. These products are processed in batches through a single pipeline system. To maintain the integrity of individual batches, the lines have to be cleaned between changeover operations. To clean the residual product a process of pigging, draining, and then flushing is conducted. Flushing involves the use of the next product being processed in the line. This results in the mixing/commingling of the residual product and the upcoming product. The commingled product cannot be used for the desired application and is therefore classified as a downgraded having to be sold as a lower-value lubricant. This results in tremendous economic losses to these industries. To this end, the focus of our work is to optimize the flushing operations and minimize the losses currently experienced by these industries. Through our student-faculty-industry collaboration program, the students received the opportunity to work with a lubricant manufacturing industry that is one of the world leaders in manufacturing finished petroleum products. The students gained an abundance of industrial experience by interacting with industry engineers and scientists through regular meetings over the course of the semester. In addition, they also scheduled several visits to the plant in order to understand and model the industrial operations. Through their plant visits the students measured and observed the complex pipeline network at the facility and developed a process flow diagram to mimic the system in the form of an experimental rig at our laboratory. Through well-designed experiments, the students applied the fundamentals of chemical engineering principles and came up with procedural enhancements to optimize the existing flushing operations at the partnered facility. The procedural improvements were then scaled up to plant scale and implemented at the facility. The students trained the plant operators to conduct the flushing operations with enhanced techniques. The improvements resulted in the minimization of the downgraded product to over 30%. Alongside the students also conducted rigorous data analysis, laboratory tests, and extensive literature reviews to enhance their creative thinking and come up with innovative solutions. Good communication and teamwork are among the most important traits needed in a good engineer. This project also gave the students an opportunity to communicate the results and ideas with a wide variety of audiences including professors, technical industry contacts, and managerial industry contacts. In addition, the students gained hands-on report writing and documentation of their work. Frequent technical presentations advanced the team's communication and soft skills. Creating these connections from industry and classroom knowledge has strengthened the team as engineers and helped prepare them for future endeavors.

Initial design stages are inherently complex and often lack comprehensive information, posing challenges in evaluating sustainability metrics. Machine Learning (ML) emerges as a valuable solution to address these challenges. ML algorithms, particularly effective in predicting environmental impacts of new chemicals with limited data, enable more informed decisions in sustainable design. This study focuses on employing ML for predicting the environmental impacts related to human health, ecosystem quality, climate change, and resource utilization to aid in early-stage environmental impact assessment of chemical processes. The effectiveness of the ML algorithm, eXtreme Gradient Boosting (XGBoost) tested using a dataset of 350 points, divided into training, testing, and validation sets. The study also includes a practical application of the model in a cradle-to-cradle LCA of N-Methylpyrrolidone (NMP), demonstrating its utility in sustainable chemical process design. This approach signifies a significant advancement in the early stages of process design, highlighting the potential of ML in enhancing environmental sustainability in the chemical industry.

With the fourth industrial revolution well underway, the proportion of occupations requiring "high" or "medium" digital skills has never been greater. Among those most in demand are engineers skilled in computing and advanced problem solving to support the ongoing digitalization, networking, and automation. A numerical analysis course in the core undergraduate engineering curriculum is a natural place for students to learn numerical methods for advanced problem solving across engineering applications. The use of computing across the entire chemical engineering curriculum also offers opportunities to hone students' abilities as computational thinkers and effective problem solvers to meet the current and future needs of an increasingly complex and digital industry and society. While the current chemical engineering curriculum includes computational training, there is a need to efficiently increase the exposure of students to computing within mathematical problem-solving contexts and develop their proficiency in computer programming, all while balancing demands to reduce credit hours. Some chemical engineering faculty interested in enhancing the computational nature of their courses face a barrier to doing so due to unfamiliarity with some modern computational educational resources that may not have been covered in their training or may not be used in their research areas. The authors developed a workshop to teach chemical engineering faculty to use and develop interactive coding templates (MATLAB Live Scripts and Jupyter Notebooks) and to equip faculty to incorporate these techniques across the undergraduate curriculum. The workshop was presented at the 2022 ASEE/AIChE Summer School for Engineering Faculty. The purpose of this paper is to disseminate the workshop resources, providing educators with a suite of interactive templates focused on chemical engineering-related case studies and with training to create and adapt their own related materials. The paper details the interactive coding templates provided during the workshop along with the relevant pedagogical background and some lessons learned for future related workshops. Educators who did not attend the workshop are also a target audience of this paper as it provides tips and access to the relevant materials for implementing computational thinking through interactive coding templates into their classroom practices.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1613 &RVW (IIHFWLYH ([SHULPHQWV LQ &KHPLFDO (QJLQHHULQJ &RUH &RXUVHV Robert P. Hesketh and C. Stewart Slater Department of Chemical Engineering Rowan University Glassboro, NJ 08028 Abstract Through funding of National Science Foundation we have developed some novel experiments that present process science principles suitable for a variety of chemical engineering core courses. These experiments are cost effective and represent some of the emerging areas: polymer processing, food processing, environmental reactor design, fluidization, membrane separation. These experiments have been utilized by chemical engineering faculty at a unique hands-on industrially integrated NSF workshop on Novel Process Science and Engineering conducted at Rowan University. We have integrated these experiments into our curriculum so that students can see chemical engineering principles in action and therefore improve the quality of education. Introduction Hands-on laboratory experience is a critical element in undergraduate chemical engineering education [Par94, Gri97]. Chemical engineering programs are often confronted with how to more effectively integrate the experimental experience more widely across the curriculum in a cost-effective manor. Some departments are also challenged with bringing laboratory experience into the Freshman year. Others are interested in presenting advanced technology or emerging fields through laboratory experiments. Typically chemical engineering laboratory experiments are presented in a Senior-level unit operations laboratory. In this setting students gain experience with many of the processes that are presented in various previous courses in the curriculum, e.g. heat exchanger, distillation column, extraction column, filter press, reverse osmosis system. In the majority of cases these are pilot-scale process units that are quite expensive and complex. A pilot scale distillation system for student costs nominally more than $100K. These experiments serve the role to give students a more realistic depiction of actual processing equipment. At Rowan we believe that it is important to integrate laboratory experience throughout our curriculum in courses that make sense pedagogically [Hes97a,b, Hes98]. These "course labs" occurs in several places and typically use a bench-scale experiment that can be performed within 2 hours. We also have multiple laboratory set-ups [Sla96] to facilitate an experimental period being conducted with a multiple groups of students running the same experiment. To facilitate a laboratory program of this nature the time, scale, complexity and cost must all be optimized and matched to the appropriate experimental setting. What we are describing in this paper is the first step in our laboratory development efforts. We will present overviews of

The techniques used to encourage young people to pursue careers in engineering are presented in this paper.The first two programs were developed by Rowan

Maintaining product integrity in multi-product oil pipelines is crucial for efficiency and profit. This study presents a strategy combining design and process improvement to enhance flushing protocols, addressing the challenge of residual batch contamination. A pilot plant, mirroring industrial operations through dimensionless residence time distribution, was developed to identify and rectify bottlenecks during product transition. The pilot plant�s success in replicating industrial operations paves the way for targeted experiments and modelling to enhance optimized flushing, ensuring product quality and operational excellence.

Commercial lubricant industries use a complex pipeline network for the sequential processing of thousands of unique products annually. Flushing is conducted between changeovers to ensure the integrity of each production batch. An upcoming product is used for cleaning the residues of the previous batch, resulting in the formation of a commingled/mixed oil that does not match the specifications of either of the two batches. The existing operations are based on the operator's experience and trial and error. After a selected flush time, the samples are tested for their viscosity to determine the success of a flush. The approach results in long downtime, the generation of large commingled oil volumes, and huge economic losses. Hence, to overcome the drawback, our work introduces a solution strategy for systematically optimizing flushing operations and making more informed decisions to improve the resource-management footprint of these industries. We use the American Petroleum Institute-Technical Data Book (API-TDB) blending correlations for calculating the mixture viscosities in real-time. The blending correlations are combined with our first-principles models and validated against well-designed experimental data from the partnered lubricant facility. Next, we formulate an optimal control problem for predicting the optimum flushing times. We solve the problem using two solution techniques viz. Pontryagin's maximum principle and discrete-time nonlinear programming. The results from both approaches are compared with well-designed experimental data, and the economic and environmental significance are discussed. The results illustrate that with the application of a discrete-time nonlinear programming solution approach, the flushing can be conducted at a customized flow rate, and the necessary flushing volume can be reduced to over 30% as compared to the trial-and-error mode of operation.

The standard vessel for mixing in the process industry is a baffled tank. This design is very common especially for low viscosity fluids. Nonetheless, unbaffled vessels are found in industry and the laboratory. The reasons given for using unbaffled vessels are primarily concerns around cleaning the vessel between batches and the possibility of stagnation zones in high viscosity fluids particularly when properties change over the processing time. In this paper we will examine the effect of baffles on the blend time using one vessel: with and without baffles. The blend time was measured using the iodine color change method using a range of viscosities and Reynolds Numbers. The depth of the vortex was also measured. The same axial flow-down pumping impeller is used. In this work we observed that the flow patterns are dramatically different between unbaffled and baffled tanks. At high Reynolds Numbers with the baffled configuration, we see the familiar turbulent diffusion throughout the vessel. Without the baffles the fluid segregates into two zones: a swirling inner zone near the shaft and a more turbulent outer zone. The overall blend time is longer in the baffled tank at the same rotational speed compared to the baffled tank. This difference decreases with increasing Reynolds Number. We identified two blend times in using unbaffled tanks, a short blend time for the outer zone and a much longer time for the inner vortex centered zone which defines the overall blend time for the vessel. At low Reynolds Numbers the baffled blending is in the transitional flow regime between turbulent and laminar with a combination of eddies and striations. The last region of the tank to become completely mixed is located behind the baffles. Unbaffled blend times are shorter and as the Reynolds Number decreases the difference increases. The vortex is also reduced with decreasing Reynolds number. We consider this work just a beginning step in a long study and hope this work will be picked up by other researchers as happened so many times with Professor Nienow’s work.

Lube-oil industries use a complex network of pipelines for transporting thousands of high-value finished products successively in batches throughout the production plant. Each lube-oil is unique in regard to its properties, and its integrity is extremely crucial. Therefore, during a changeover operation, the lines are flushed using a high-value finished product of the current batch that is desired to be processed. The existing flushing operation typically rely on a trial-and-error procedure, resulting in the downgrading of the finished product. Moreover, it leads to enormous economic losses to the industries. In response to this problem, this work presents an approach for modeling and optimizing the flushing operation by employing first-principles and optimal control strategies. We model the flushing operation by integrating the Kendall and Monroe viscosity blending equations with time-dependent component balance equations for lube-oil pipelines. The models developed are validated against the data collected from well-designed flush-study experiments, and a good agreement is observed. We generate theoretical optimal flowrate profiles and provide insights for designing and controlling the flushing operation.

A heterogenous Palladium anchored Resorcinol-formaldehyde-hyperbranched PEI mesoporous catalyst, made by one-pot synthesis, was used successfully for in situ Suzuki-Miyaura cross coupling synthesis of anticancer prodrug PP-121 from iodoprazole and boronic ester precursors. The mesoporous catalyst with the non-cytotoxic precursors were tested in 2D in vitro model with excellent cytocompatibility and a strong suppression of PC3 cancer cell proliferation, underscored by 50% reduction in PC3 cells viability and 55% reduction in cell metabolism activity and an enhanced rate of early and late apoptosis in flow cytometry, that was induced only by successful in situ pro drug PP121 synthesis from the precursors. The 3D gelatin methacrylate hydrogel encapsulated in vitro cell models underscored the results with a 52% reduction in cell metabolism and underscored apoptosis of PC3 cells when the Pd anchored catalyst was combined with the precursors. In situ application of Suzuki-Miyaura cross coupling of non-cytotoxic precursors to cancer drug, along with their successful encapsulation in an injectable hydrogel could be applied for tumor point drug delivery strategies that can circumvent deleterious side effects and poor bioavailability chemotherapy routes with concomitant enhanced efficacy.

A semi-empirical model was applied to evaluate the performance of a vibratory nanofiltration (NF) system, using 150-Da TS80 NF membrane, for the preconcentration of coffee extracts in soluble coffee processing. The effects of transmembrane pressure (TMP), feed concentration, and module vibration on flux enhancement were correlated with membrane surface concentrations and fouling resistances under steady state operation. Vibratory shear thinned the boundary layer and increased the mass transfer coefficient of the solvent (water) by a factor of 3.5. Membrane surface concentrations and fouling resistances reduced by 60% compared with crossflow (CF) NF operation. These reductions enhanced permeate fluxes by about 2-3 times that of CF operation, with low flux decline. Feed concentration and TMP promoted polarization more than the negative effect of vibration. Osmotic pressure resistances were dominant under low feed concentrations and TMP. However, concentration polarization resistances exceeded osmotic pressure resistances as TMP and feed concentrations were increased. Real rejections relative to membrane surface chemical oxygen demand (COD) were above 0.99, indicating the potential of the operation to recover permeate that is reusable for ancillary plant operations. Overall, the experimental and theoretical permeate fluxes and CODs were in reasonable agreement, indicating the reliability of the model. Practical Applications Vibratory membrane processes alleviate the issues on membrane fouling that is advantageous when integrated into food and beverage processes. Its application as a coffee extract preconcentration alternative to thermal evaporation opens opportunities for sustainable soluble coffee production that can be adapted in other food and beverage industries. However, the unique dynamic nature of the vibratory membrane system challenges conventional approaches for understanding and predicting the mechanisms of the process. This gap limits the overall transferability of the technology to broader industry sectors. The semi-empirical resistance-in-series model developed in this study correlates the important factors with the vibratory NF performance based on fundamental concepts: concentration polarization, osmotic pressure effects, and fouling resistance. The model is not only useful in managing membrane fouling in vibratory systems, but also in optimizing and developing alternative approaches on similar lines and their scale-up to promote other industrial applications.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Academic - Industrial Partnerships to Advance Pollution Prevention Abstract Student projects have examined how to apply pollution prevention strategies to both R&D and manufacturing in several chemical industries. This has been accomplished through industry-university partnerships with pharmaceutical and petrochemical companies. Several grants from the US Environmental Protection Agency have supported initiatives in green chemistry, engineering and design. These projects have the broader goal of supporting sustainability in the chemical industry. Introduction Too often the teaching of a technical subject like green engineering is limited to an individual class experience or one dimensional laboratory or design experience. The teaching of pollution prevention in the curriculum is greatly enhanced by active participation of students throughout the curriculum and in real-world projects. Green engineering is a multidisciplinary topic that if practiced to the fullest would greatly improve how industry operates and provide a sustainable future. Rowan University is incorporating green engineering into its curricula in various course and our latest efforts (as described in this paper) are to actively involve industry in green engineering projects through our engineering clinic program. In this paper we refer to the terms pollution prevention and green engineering interchangeably. Green chemistry and engineering methods are forms of pollution prevention. EPA defines pollution prevention/source reduction as any practice which does one or more of the following: • Reduces the amount of any hazardous substance, pollutant, or contaminant entering any waste stream or otherwise released into the environment (including fugitive emissions) prior to recycling, treatment or disposal. • Reduces the hazards to public health and the environment associated with the release of such substances, pollutants, or contaminants. • Reduces or eliminates the creation of pollutants through - increased efficiency in the use of raw materials, energy, water, or other resources; or - protection of natural resources by conservation. Specifically this paper addresses pollution prevention through green chemistry, green engineering and design for the environment strategies. The EPA originally defined green engineering as the design, commercialization and use of processes and products that are feasible and economical while minimizing the generation of pollution at the source and also minimizing risk to human health and the environment [1]. The

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1526 A Hands-on Workshop on Novel Process Engineering C. Stewart Slater and Robert P. Hesketh Department of Chemical Engineering Rowan University Glassboro, NJ 08028 Abstract This paper describes a NSF-funded Undergraduate Faculty Enhancement Workshop on Novel Process Science and Engineering. The project DUE-9752789 supports two hands-on, industry integrated workshops that will have a major impact on upper and lower level engineering, technology and science instruction as well as having a secondary impact in the preparation of future teachers. Two workshops were held in July 1998 and July 1999. Participants gained experience in process engineering through hands-on laboratories, industry experts, and interactive demonstrations. Through industry involvement from 10 process engineering companies, faculty were given an initial networking base. Companies contributing industrial speakers include Sony Music, Inductotherm, DuPont Engineering, Bristol-Myers Squibb, Chemical Industry Council of New Jersey, Cochrane, Tasty Baking Co., DuPont Pharmaceuticals, DuPont Nylon, AstraZeneca Pharmaceuticals, AE Technology-Hyprotech, and Mobil Technology Co. Participants use the given methodology to integrate novel processing into their curricula and develop an action plan for their home institution. Active learning methods were employed in the workshop and participants were encouraged to incorporate this experience into their teaching style. Introduction Two innovative and state-of-the-art workshops on the multidisciplinary aspects of novel process science and engineering were held at Rowan University, Glassboro, New Jersey, July 26- 30, 1998 and July 18-22, 1999. These workshops are one of the many excellent programs supported by the National Science Foundation’s Undergraduate Faculty Enhancement Program. The purpose of the workshops is to meet the needs of faculty who teach undergraduate students. Of particular importance are programs that expose faculty to recent technological developments and present methods to incorporate them into the undergraduate curriculum. Process engineering is critical to virtually all modern products used by society. In addition, process engineering spans many disciplines including chemical, petroleum, biochemical, environmental, food, materials production and manufacturing. In many cases the interface of

Process simulators are being used extensively in senior level chemical engineering design courses, and are becoming more prevalent in lower level courses. This paper explores the impact of chemical engineering programs starting to integrate process simulators throughout the curriculum. We will assess the features of process simulators that are easy to use and are effective in communicating chemical process principles. In addition, we will examine aspects of simulators that are difficult for students to comprehend, use and result in a poor utilization of educational resources. What are the possibilities for courses that traditionally do not use process simulators because standard models have not been incorporated in process simulation? Another aspect that will be discussed will be that many graduates will work for companies that do not currently use process simulators. In many cases these companies include future growth opportunities for chemical engineers including pharmaceuticals, bioprocessing and membrane applications. If a large percentage of students that are trained in process simulators do not use them, then is integrating process simulation an effective use of educational time at the undergraduate level? What issues are brought about when students become dependent on process simulation results and are not able to perform hand calculations for an industry without process simulation?

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session Number 3413 INTRODUCING FRESHMEN TO DRUG DELIVERY Stephanie Farrell and Robert P. Hesketh Chemical Engineering Department Rowan University 201 Mullica Hill Road Glassboro, New Jersey 08028-1701 Abstract Drug delivery is an exciting multidisciplinary field in which chemical engineers play an important role. Chemical engineers apply their knowledge of mass transfer, rates and dynamic systems, and polymer materials to the design of drug delivery systems. This paper describes a simple experiment that exposes students to basic principles of drug delivery and chemical engineering. First, students are introduced to different types of dosage formulations using as examples over-the-counter-medications that are already familiar to the students. The mechanism of drug release is different for each type of formulation, and students learn how each different dosage form works. The students then perform an experiment that involves the release a drug from a lozenge formulation, which is an example of a matrix-type drug delivery system. Students study the dissolution of a lozenge into water. As the lozenge dissolves, the drug is released, along with a coloring agent, into the surrounding water. Students observe the increasing color intensity of the water, and they are able to measure the increasing drug concentration periodically using a spectrophotometer. After calculating the mass of drug released at any time t, they plot a release profile. They must calculate by material balance the mass of drug remaining in the lozenge at any time. They are also able to compare their data to a model after evaluating a single parameter in the model. Through this experiment, students are exposed to the exciting field of drug delivery, and they are introduced to some basic principles of chemical engineering. They perform a calibration to enable them to determine the concentration of drug in their samples. A spreadsheet is used to perform calculations necessary to determine the release profile, and a plot of the release profile of drug from their lozenge is created. Finally they determine the parameter necessary to apply a model to their system, and they compare their experimental release profile to that described by the model. Introduction Rowan University is pioneering a progressive and innovative Engineering program that uses innovative methods of teaching and learning to prepare students better for a rapidly changing and highly competitive marketplace, as recommended by ASEE[1]. Key features of the program include: (i) multidisciplinary education through collaborative laboratory and course work; (ii) teamwork as the necessary framework for solving complex problems; (iii) incorporation of state-