Conference Agenda

Biomass is the sole renewable organic carbon source found in nature, as such its conversion to chemical derivatives and essential intermediates is studied as a long-term strategy for the chemical sector. Among the numerous valuable chemicals obtained from biomass, 5-hydroxymethylfurfural (HMF) is considered an industrially relevant compound due to its capacity to be converted into a variety of value-added chemicals. HMF hydrogenation and amination products stand out, owing to their high demand and versatility, as platform chemicals for sustainable polymeric materials and pharmaceuticals, such as 2,5-bis(aminomethyl)furan (BHMF) and 5-(aminomethyl)-2-furanmethanol (AMF).

Compared to conventional catalytic synthesis, bio-catalysis has emerged as a potential greener substitute for HMF conversion to value-added compounds. Enzymatic bio-catalysis operates in milder circumstances; however, the reactions are often incomplete, need longer incubation periods while offering lower yields. Hydrogenation of HMF has been thoroughly investigated, however few studies focused on the amination, while seldom do any of these studies present the separation and purification process of the obtained high added-value products. The current study focuses on the enzymatic conversion of HMF to high added-value chemicals, such as BHMF and AMF, when phenethylamine is utilized as amine donor. Considering the full conversion of HMF, four compounds are stoichiometrically obtained in the reaction mixture, i.e., BHMF, AMF, phenethylamine and phenethyl alcohol. The separation and purification processes are designed by means of rigorous simulations carried out in Aspen Plus V11.

The first step in the separation of the mentioned compounds is a cationic ion exchange process to separate the amines and alcohols, followed by the neutralization of both streams. The neutralization process results in the formation of mineral salts which can be removed by means of crystallization and filtration. The resulting aqueous solution of the two amines was separated through fractional distillation, obtaining AMF as bottom product with >98 wt.% purity and phenethylamine as side product with >98 wt.% purity. As for the separation of the alcohols, distillation process is utilized to obtain BHMF as bottom product with >99 wt.% purity. The aqueous solution of phenethyl alcohol is introduced in a liquid-liquid extraction section, followed by a distillation process where over 99 wt.% phenethyl alcohol is obtained at the bottom, and over 99 wt.% hexane at the top of the column. The global specific energy demand for the manufacture of a kg of product is approximately 1.79 kWh/kg.

The novelty of the research consists in the development of a new amination-based option for the enzymatic bio-catalytic conversion of HMF to BHMF and AMF (experimentally proven), and the design by process simulations of a feasible path for the separation of intermediates and products, by a cost effective and eco-efficient combination of crystallization, filtration, distillation, liquid-liquid extraction and ion-exchange unit operations.

Ethyl Acetate (EtAc) and Methanol (MeOH) are among the most frequently used organic solvents in the manufacturing of pharmaceuticals [1]. With growing environmental and sustainability concerns, newer regulations are pushing the industry to recover the organic solvents from the often dilute aqueous waste solvent and reuse the same for a “green” manufacturing process [2]. In this overall context, this study explores the design and synthesis of a two-column distillation (TCD) process to separate a dilute ternary EtAc-MeOH-water waste solvent into nearly pure components. The separation is complicated by the presence of a homogeneous EtAc-MeOH azeotrope and a heterogeneous EtAc-water azeotrope, which results in a distillation boundary that partition the ternary composition space into two distinct distillation regions. The proposed flowsheet leverages liquid-liquid phase separation to cross the distillation boundary for separation feasibility. Also, the pressure sensitivity of the distillation boundary is utilized to reduce the total recycle rate for energy efficiency. The basic TCD flowsheet, which consists of a decanter, a high-pressure simple column, and a low-pressure divided-wall column (DWC), is heat-integrated (HI) using external heat process-to-process exchangers as well as vapour recompression (HR)driven reboilers on the two columns. The resulting energy-efficient HIVR-TCD configuration is significantly cheaper and energy-efficient compared to existing literature designs [3]. Specifically, the total annualized cost (TAC) of the proposed HIVR-TCD process design is 15.4% lower than a three-column HIVR design recently reported in the literature. Also, the energy consumption and CO₂ emissions are lower by 34.3% and 31.4%, respectively, which represents a significant improvement.

References

[1] C.-G. ,. K. H. D. J Constable, "Perspective on solvent use in the pharmaceutical industry," Organic process research & development, vol. 11, pp. 133-137, 2007.

[2] L. P.-B. C. J. García-Serna, "New trends for design towards sustainability in chemical engineering: Green engineering," Chemical Engineering Journal, vol. 133, no. 1-3, pp. 7-30, 2007.

[3] A. Yang, S. Sun, Z. Y. Kong, S. Zhu, J. Sunarso and W. Shen, "Energy-efficient heterogeneous triple-column azeotropic distillation process for recovery of ethyl acetate and methanol from waste water," Computers and Chemical Engineering, p. 108618, 2024.

Distillation is an important separation technique and widely employed in the chemical industry. With batch distillation, separation of multi-component mixture is achieved using a single column where products are recovered in a particular sequence. Conventionally, the feed is added in the reboiler and products are withdrawn as distillate in the decreasing order of their volatilities (direct sequence). On the other hand, in an inverse configuration, the feed is added in the reflux drum and the products are withdrawn from the bottom in the increasing order of volatility (indirect sequence). Although these sequences yield the same products, they exhibit different capital and operating costs. A key question in the design of batch distillation processes is to obtain the optimal sequence of separation. The existing literature in this context either uses heuristics or rigorous optimization [1]. As the number of components to be separated increase, the number of potential separation sequence alternatives increase drastically, necessitating a systematic approach to obtain the best sequence. Motivated by this, present work aims at obtaining these sequences in a generic and computationally efficient manner.

In continuous distillation of multi-component mixtures, marginal vapor rate method is used to obtain the best separation sequence [2]. In our approach, this method is extended for batch separation. There are two major challenges for this extension. Firstly, unlike steady state operation in continuous distillation, the feed as well as product composition change as a function of time in batch distillation. Secondly, instead of molar flow rate, there are material balance constraints on total moles. These two challenges are addressed in this work by approximating the batch distillation task as a sequence of continuous distillation tasks with varying feed composition and making simplifying assumption like negligible tray holdup [3]. Accordingly, marginal vapor is computed for each binary separation by integrating the corresponding marginal vapor rate over batch time. Subsequently, the marginal vapours for each separation task are added together to obtain total marginal vapor for a particular separation sequence. The sequence with the lower marginal vapor is recommended as the best sequence.

The proposed approach offers computational advantage over rigorous optimization as well as helps identifying optimal non-trivial separation sequences. The methodology is validated with simulation case studies involving separation of ternary and quaternary mixtures.

References

[1] E. Sørense and S. Skogestad, “Comparison of regular and iverted batch distillation,” Chemical Engineering Science, p. 13, 1996.

[2] A. K. Modi and A. W. Westerberg, “Distillation column sequencing using marginal price,” Ind. Eng. Chem. Res., p. 9, 1992.

[3] U. Diwekar, Batch Distillation: simulation, optimal design and control, 2011.

Acrylonitrile (AN) is a critical commodity chemical used to produce a variety of industrial polymers, such as carbon fibers, plastics, rubber, etc. Standard Oil of Ohio (SOHIO) process is the current commercial process for AN production based on propylene ammoxidation and accounts for over 90% of global acrylonitrile production. However, this process involves several distillation columns in the downstream separation, which is energy demanding due to the low thermal efficiency of distillation columns. Nonetheless, the propylene ammoxidation highly exothermic (△H = –123 kcal/mol). Ideally, this reaction heat from the upstream reactor could be utilized and integrated with the downstream separation. Given the current rise in energy costs, and the increased environmental concerns, designing an energy integrated and more sustainable process for the acrylonitrile production is of great importance.

This original study is the first to provide a rigorous process design of the full process from a holistic viewpoint, covering all sections of acrylonitrile production: reaction, acid quenching, absorption-desorption, hydrogen cyanide recovery, acrolein recovery, acrylonitrile-acetonitrile-water separation, and acetonitrile recovery sections. Furthermore, in order to improve the energy efficiency and the sustainability metrics such as greenhouse emissions of the developed process, four energy integration options with different focuses are developed: (1) Implement process intensification technologies for downstream separations, including pressure-swing distillation with full-heat integration, dividing wall columns, etc., exploring also the mechanical vapor recompression heat-pumping potentials. (2) Synthesize the heat exchanger network (HEN) for simultaneous optimization of direct heat integration and heat pumps, compare the optimum design with the results from pinch analysis. (3) Remove reactor surplus heat by hot oil and incorporate it as a new hot utility with the rest HEN, optimize the new HEN to fully utilize this new heat source. (4) Use the surplus heat from the reactor to generate power by downgrading the heat from very high pressure steam (VHP, 100 bar) to low pressure steam (LP, 6 bar), and then used the LP steam to satisfy the heating requirements in the process.

Thermodynamic analysis, sensitivity analysis, economic and sustainability assessments are carried out for option 1, and results show that the heat integrated intensified process enables 27.27% energy savings and 28.20% reduction of greenhouse gas emissions. By doing heat integration systematically in option 2, the optimum HEN shows 60% energy savings as compared to the non-heat integrated intensified process. In option 3, by utilizing the heat of reaction, the optimum HEN is provided in which no external hot utility is required. As for the option 4, using the reaction surplus heat can generate 7 MW power (to be exported) and 48 MW LP steam (to be utilized as the downstream hot utility). The advantages and disadvantages of each approach are analyzed and evaluated, leading to industrial guidance. As the first complete and comprehensive description of the design and optimization of the entire acrylonitrile production process via the SOHIO method, this work highlights the potential for improved energy efficiency in the acrylonitrile production, with the proposed process representing a major step towards achieving these goals.

The world efforts towards achieving net zero include the development of a mix technologies aiming at the sustainable production and storage of energy. A share of these efforts is focused on the development of Li-ion batteries, whose utilisation has grown in multiple applications like electric portable devices, EVs and storage due to their ability to store and deliver energy over cycles of use. The performance and cost of the batteries relates to the properties of the cathode materials. Several chemistries have been researched to improve the performance of cathode active materials including the use of lithium-iron phosphate, nickel-manganese-cobalt and nickel-manganese-aluminium. In this work, we look at the manufacturing of combined hydroxides of nickel-manganese-cobalt.

This investigation addresses the mathematical modelling of the co-precipitation of combined hydroxides of nickel, manganese, and cobalt (NMC), particularly with stoichiometry 8:1:1. The mathematical model considers a lab-scale stirred semi-batch crystalliser fed with solutions of transition metals sulphates (i.e., nickel, manganese, and cobalt), precipitant agent (i.e., sodium hydroxide) and chelating agent (i.e., ammonium hydroxide).

The modelling methodology involved the application of mass balances for the ionic species in the reactor, along with the application of the population balance equation to track the number of particles produced out of the precipitation process as well as the particle size distribution of the product. The following assumptions were considered during the modelling efforts:

1. The precipitated crystals are spherical.
2. A clear solution is utilised at initial conditions and no crystals are present in any of the feeds.
3. The reactor/crystalliser is well mixed; hence, no mass transfer limitations occur during the formation of primary and secondary particles.
4. Particle growth is independent of crystal size.
5. The precipitation of individual transition metal hydroxides does not occur.
6. The reaction rate of both the ionic complexes and the hydroxide formation are fast; the controlling mechanisms of the process are the nucleation and growth of the crystals.
7. The crystalliser operates isothermally.
8. Agglomeration and breakage of particles are neglected.

The produced model is a set of 30 integral, partial, differential, and algebraic equations (IPDAEs); it is implemented and solved in the commercial solver Aspen Custom Modeler utilising the method of lines and an upwind scheme to discretise the spatial differentials of the population balance equation.

The model testing, and a sensitivity analysis of the crystallisation kinetic parameters were carried out over the range 10⁴<k_b<10¹⁰, 1<b<5, 10^-10<k_g<10^-6 and 1<g<2.5 and successfully reproduced the expected features of the process regarding particle size distribution, mean particle size, supersaturation and the evolution of the concentration profiles of the ionic species present in the system. Moreover, the model was qualitatively compared against experimental data available for ranges of pH of 10.4 – 11.0 and concentration of the chelating agent of 0.3 – 1.5M, rendering a good agreement with the experimental data.

The development and production of pharmaceutical products is governed by stringent regulations that necessitate a comprehensive understanding of manufacturing processes. This understanding encompasses the impact of the critical material attributes (CMAs) as well as the critical process parameters (CPPs), which define the operational conditions during production that significantly influence critical quality attributes (CQAs) of the final product. Establishing a design space (DS), a multidimensional framework that captures acceptable variances in CMAs and CPPs, enables manufacturers to optimize processes while ensuring consistent product quality (Peterson 2008). However, the reliability of any DS determined through a process model is contingent upon the accuracy of that model. If the uncertain parameters of the model follow a specific probability distribution, the design space becomes probabilistic rather than clearly defined (Kusumo et al. 2020). This shift may necessitate changes to the DS and could trigger a regulatory process for postapproval changes; however, such changes are not required as long as the process parameters remain within the limits of the approved DS.

Here, the probabilistic design space is constructed for a tableting process by propagating the model parameter uncertainty. In the process, lubrication extent (27-6736) and porosity (0.09 - 0.30) are identified as the CPPs, while the tablet tensile strength is designated as the CQA. The empirical model developed by Nassar et al. (2019) is used to capture the impact of CPPs by involving five unknown parameters (θ = [a₁ (MPa), a2 (-), b1 (-), b2 (-), and g (dm^-1)]. The uncertainty of these model parameters is represented by a sampled distribution (mean = [10, 1.2, -6.8, 0.42, 0.0022], variance = [0.425, 0.2875, 0.3, 0.045, 0.000325]), enabling the application of Monte Carlo and Bayesian techniques to propagate this model parameter uncertainty to the CQAs and estimate the feasibility probability for achieving a reliability value greater than 0.9. This probabilistic design space allows manufacturers to assess the likelihood of meeting CQAs under varying conditions, further emphasizing its importance in facilitating regulatory compliance. Additionally, integrating flexibility analysis provides a comprehensive assessment of the tableting process's ability to adapt to changes in critical process parameters (CPPs) while still achieving the desired CQAs. Preliminary findings suggest the identification of a robust design space defined by specific combinations of lubrication rate and porosity, which ensure exceptional tableting performance even in the presence of uncertainties.