Strategic Optimization of the Flushing Operations in Lubricant Manufacturing and Packaging Facilities.

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.

A generic scenario analysis of end-of-life plastic management: Chemical additives.

Plastic growing demand and the increment in global plastics production have raised the number of spent plastics, out of which over 90% are either landfilled or incinerated. Both methods for handling spent plastics are susceptible to releasing toxic substances, damaging air, water, soil, organisms, and public health. Improvements to the existing infrastructure for plastics management are needed to limit chemical additive release and exposure resulting from the end-of-life (EoL) stage. This article analyzes the current plastic waste management infrastructure and identifies chemical additive releases through a material flow analysis. Additionally, we performed a facility-level generic scenario analysis of the current U.S. EoL stage of plastic additives to track and estimate their potential migration, releases, and occupational exposure. Potential scenarios were analyzed through sensitivity analysis to examine the merit of increasing recycling rates, using chemical recycling, and implementing additive extraction post-recycling. Our analyses identified that the current state of plastic EoL management possesses high mass flow intensity toward incineration and landfilling. Although maximizing the plastic recycling rate is a reasonably straightforward goal for enhancing material circularity, the conventional mechanical recycling method requires improvement because major chemical additive release and contamination routes act as obstacles to achieving high-quality plastics for future reuse and should be mitigated through chemical recycling and additive extraction. The potential hazards and risks identified in this research create an opportunity to design a safer closed-loop plastic recycling infrastructure to handle additives strategically and support sustainable materials management efforts to transform the US plastic economy from linear to circular.

Development of an Interactive Software Tool for Designing Solvent Recovery Processes.

Solvents are used in chemical and pharmaceutical industries as a reaction medium, selective dissolution and extraction media, and dilution agents. Thus, a sizable amount of solvent waste is generated due to process inefficiencies. Most common ways of handling solvent waste are on-site, off-site disposal, and incineration, which have a considerable negative environmental impact. Solvent recovery is typically not used because of potential difficulties in achieving required purity guidelines, as well as additional infrastructure and investments that are needed. To this end, this problem must be studied carefully by involving aspects from capital needs, environmental benefits, and comparison with traditional disposal methods, while achieving the required purity. Thus, we have developed a user-friendly software tool that allows engineers to easily access solvent recovery options and predict an economical and environmentally favorable strategy, given a solvent-containing waste stream. This consists of a maximal process flow diagram that encompasses multiple stages of separations and technologies within those stages. This process flow diagram develops the superstructure that provides multiple technology pathway options for any solvent waste stream. Separation technologies are placed in different stages; depending on the component, they can separate in terms of their physical and chemical properties. A comprehensive chemical database is created to store all relevant chemical and physical properties. The pathway prediction is modeled as an economic optimization problem in General Algebraic Modeling Systems (GAMS). With GAMS code as the backend, a Graphical User Interface (GUI) is created in Matlab App Designer to provide a user-friendly tool to the chemical industry. This tool can act as a guidance system to assist professional engineers and provide an easy comparative estimate in the early stages of process design.

Systems level roadmap for solvent recovery and reuse in industries.

Recovering waste solvent for reuse presents an excellent alternative to improving the greenness of industrial processes. Implementing solvent recovery practices in the chemical industry is necessary, given the increasing focus on sustainability to promote a circular economy. However, the systematic design of recovery processes is a daunting task due to the complexities associated with waste stream composition, techno-economic analysis, and environmental assessment. Furthermore, the challenges to satisfy the desired product specifications, particularly in pharmaceuticals and specialty chemical industries, may also deter solvent recovery and reuse practices. To this end, this review presents a systems-level approach including various methodologies that can be implemented to design and evaluate efficient solvent recovery pathways.

Synthesis and analysis of separation networks for the recovery of intracellular chemicals generated from microbial-based conversions.

BACKGROUND: Bioseparations can contribute to more than 70% in the total production cost of a bio-based chemical, and if the desired chemical is localized intracellularly, there can be additional challenges associated with its recovery. Based on the properties of the desired chemical and other components in the stream, there can be multiple feasible options for product recovery. These options are composed of several alternative technologies, performing similar tasks. The suitability of a technology for a particular chemical depends on (1) its performance parameters, such as separation efficiency; (2) cost or amount of added separating agent; (3) properties of the bioreactor effluent (e.g., biomass titer, product content); and (4) final product specifications. Our goal is to first synthesize alternative separation options and then analyze how technology selection affects the overall process economics. To achieve this, we propose an optimization-based framework that helps in identifying the critical technologies and parameters. RESULTS: We study the separation networks for two representative classes of chemicals based on their properties. The separation network is divided into three stages: cell and product isolation (stage I), product concentration (II), and product purification and refining (III). Each stage exploits differences in specific product properties for achieving the desired product quality. The cost contribution analysis for the two cases (intracellular insoluble and intracellular soluble) reveals that stage I is the key cost contributor (>70% of the overall cost). Further analysis suggests that changes in input conditions and technology performance parameters lead to new designs primarily in stage I. CONCLUSIONS: The proposed framework provides significant insights for technology selection and assists in making informed decisions regarding technologies that should be used in combination for a given set of stream/product properties and final output specifications. Additionally, the parametric sensitivity provides an opportunity to make crucial design and selection decisions in a comprehensive and rational manner. This will prove valuable in the selection of chemicals to be produced using bioconversions (bioproducts) as well as in creating better bioseparation flow sheets for detailed economic assessment and process implementation on the commercial scale.

A roadmap for the synthesis of separation networks for the recovery of bio-based chemicals: Matching biological and process feasibility.

Microbial conversion of renewable feedstocks to high-value chemicals is an attractive alternative to current petrochemical processes because it offers the potential to reduce net CO2 emissions and integrate with bioremediation objectives. Microbes have been genetically engineered to produce a growing number of high-value chemicals in sufficient titer, rate, and yield from renewable feedstocks. However, high-yield bioconversion is only one aspect of an economically viable process. Separation of biologically synthesized chemicals from process streams is a major challenge that can contribute to >70% of the total production costs. Thus, process feasibility is dependent upon the efficient selection of separation technologies. This selection is dependent on upstream processing or biological parameters, such as microbial species, product titer and yield, and localization. Our goal is to present a roadmap for selection of appropriate technologies and generation of separation schemes for efficient recovery of bio-based chemicals by utilizing information from upstream processing, separation science and commercial requirements. To achieve this, we use a separation system comprising of three stages: (I) cell and product isolation, (II) product concentration, and (III) product purification and refinement. In each stage, we review the technology alternatives available for different tasks in terms of separation principles, important operating conditions, performance parameters, advantages and disadvantages. We generate separation schemes based on product localization and its solubility in water, the two most distinguishing properties. Subsequently, we present ideas for simplification of these schemes based on additional properties, such as physical state, density, volatility, and intended use. This simplification selectively narrows down the technology options and can be used for systematic process synthesis and optimal recovery of bio-based chemicals.

Uncertainty in clinical data and stochastic model for in vitro fertilization.

In vitro fertilization (IVF) is the most widely used technique in assisted reproductive technologies (ART). It has been divided into four stages; (i) superovulation, (ii) egg retrieval, (iii) insemination/fertilization and (iv) embryo transfer. The first stage of superovulation is a drug induced method to enable multiple ovulation, i.e., multiple follicle growth to oocytes or matured follicles in a single menstrual cycle. IVF being a medical procedure that aims at manipulating the biological functions in the human body is subjected to inherent sources of uncertainty and variability. Also, the interplay of hormones with the natural functioning of the ovaries to stimulate multiple ovulation as against single ovulation in a normal menstrual cycle makes the procedure dependent on several factors like the patient's condition in terms of cause of infertility, actual ovarian function, responsiveness to the medication, etc. The treatment requires continuous monitoring and testing and this can give rise to errors in observations and reports. These uncertainties are present in the form of measurement noise in the clinical data. Thus, it becomes essential to look at the process noise and account for it to build better representative models for follicle growth. The purpose of this work is to come up with a robust model which can project the superovulation cycle outcome based on the hormonal doses and patient response in a better way in presence of uncertainty. The stochastic model results in better projection of the cycle outcomes for the patients where the deterministic model has some deviations from the clinical observations and the growth term value is not within the range of '0.3-0.6'. It was found that the prediction accuracy was enhanced by more than 70% for two patients by using the stochastic model projections. Also, in patients where the prediction accuracy did not increase significantly, a better match with the trend of the clinical data was observed in case of the stochastic model projections as compared to their deterministic counterparts.

Optimal control for predicting customized drug dosage for superovulation stage of in vitro fertilization.

in vitro fertilization (IVF) is one of the most highly pursued assisted reproductive technologies (ART) worldwide. IVF procedure is divided into four stages: Superovulation, Egg-retrieval, Insemination/Fertilization and Embryo transfer. Among these superovulation is the most crucial stage since it involves external injection of hormones to stimulate development and maturation of multiple follicles or oocytes. Although numerous advancements have been made in IVF procedures, little attention has been given to modifying the existing protocols based on a 'patient specific' predictive model. A model for follicle growth and number change as a function of the injected hormones and patient characteristics has been developed and validated for data available on 50 superovulation cycles. The model has 9 patient specific parameters which can be determined from the initial 2 days of observation and can help in projecting the superovulation outcome for the ongoing cycle. Based on this model, the dosage of the hormones to stimulate multiple ovulation or follicle growth is predicted by using the theory of optimal control. The objective of successful superovulation is to obtain maximum number of mature oocytes/follicles within a particular size range. Using the mathematical model of follicle growth dynamics and optimal control theory, optimal dose and frequency of medication customized for each patient (n=5) is predicted for obtaining the desired result. The results indicate a better final day follicle size distribution when the dosage of the hormones is varied by some amounts as compared to the actual dosage given to the patient in the existing cycles. This ensures a better success rate for the superovulation cycles and reduces the costs of excess medication and daily monitoring. The idea is to provide the medical practitioners with a guideline for planned treatment, for a procedure currently based on trial and error in order to get better success rates.

Modeling the superovulation stage in in vitro fertilization.

In vitro fertilization (IVF) is the most common technique in assisted reproductive technology and in most cases the last resort for infertility treatment. It has four basic stages: superovulation, egg retrieval, insemination/fertilization, and embryo transfer. Superovulation is a drug-induced method to enable multiple ovulation per menstrual cycle. The success of IVF majorly depends upon successful superovulation, defined by the number and similar quality of eggs retrieved in a cycle. Modeling the superovulation stage can help in predicting the outcomes of IVF before the cycle is complete. In this paper, we developed a model for superovulation stage. The model is adapted from the theory of batch crystallization. The aim of crystallization is to get maximum crystals of similar size and purity, while superovulation aims at eggs of similar quality and size. The rate of crystallization and superovulation are both dependent on the process conditions and varies with time. The kinetics of follicle growth is modeled as a function of injected hormones and the follicle properties are represented in terms of the moments. The results from the model prediction were verified with the known data from Jijamata Hospital, Nanded, India. The predictions were found to be in agreement with the actual observations.