An Alternative Green Solvent for 1,3-Butadiene Extraction

Abstract

The separation via the extractive distillation of 1,3-butadiene from C₄ hydrocarbon mixtures is an essential step in synthetic rubber and plastic production. Conventional extractive distillation methods rely on solvents such as N,N-dimethylformamide (DMF) and N-methyl-2-pyrrolidone (NMP), which, despite their efficiency, pose significant environmental and health risks. This study investigates the feasibility of replacing these hazardous solvents with 1,2-propylene carbonate (PC), a greener alternative that aligns with REACH restrictions and CEFIC recommendations. The adoption of green solvents closely follows the UN’s Sustainable Development Goals (SDGs). Indeed, by using green solvents, industries reduce pollution, enhance worker safety, and minimize their environmental impact, contributing to multiple SDGs, and thus fostering sustainable economic growth. Advanced solvent screening methodologies, based on thermodynamic solution models (NRTL-RK) and quantum-based approaches (COSMO-RS), were employed to evaluate PC’s viability. Aspen Plus^® simulations were conducted to evaluate the industrial feasibility of PC in the 1,3-butadiene separation process. The results indicate that PC achieves comparable 1,3-butadiene separation efficiency while offering economic, operational, and environmental benefits. These findings underscore the importance of integrating sustainable solvents into industrial processes, reducing reliance on hazardous chemicals, improving compliance with evolving regulatory frameworks, and supporting sustainable industrial development.

1. Introduction

The compound 1,3-butadiene is an essential building block in the production of synthetic rubbers and plastics such as styrene–butadiene rubber, polybutadiene rubber, and latex. It is primarily obtained as a by-product of steam cracking processes, yet its separation from C₄ hydrocarbon mixtures presents considerable technical challenges due to the similar boiling points of the components within these mixtures. Conventional distillation methods require an impractically high number of trays and elevated reflux ratios, making the separation process unfeasible through conventional distillation alone [1]. Extractive distillation has emerged as the primary method for effectively separating 1,3-butadiene, capitalizing on the selective solubility and enhanced relative volatility provided by the addition of appropriate solvents to facilitate the separation process.

Historically, N,N-dimethylformamide (DMF) and N-methyl-2-pyrrolidone (NMP) have been the most widely used solvents in 1,3-butadiene extractive distillation. As aprotic dipolar solvents, they are particularly effective for this process due to their strong dipole moments and ability to induce favorable solvation interactions [2]. While 1,3-butadiene is a non-polar molecule, its conjugated diene structure makes it more polarizable than simple alkanes. The key mechanism at play is dipole-induced dipole interactions, where the high dipole moment of these solvents induces a temporary dipole in 1,3-butadiene, leading to enhanced solubility.

Each of these solvents offers distinct advantages and limitations [2,3,4,5,6]. However, despite their performance advantages, these solvents are associated with severe health and environmental risks. Prolonged exposure to DMF and NMP has been linked to adverse health effects, including skin irritation, liver toxicity, and increased cancer risks. Furthermore, these solvents may bioaccumulate in ecosystems, posing a long-term threat to biodiversity and environmental stability. Given these concerns, there is a pressing need to transition towards greener and safer alternatives to ensure sustainable industrial processes and responsible resource use [7,8].

The economic, environmental, and health concerns linked to the conventional solvents used in extractive distillation have fueled increasing interest in simulation-based approaches to evaluate and enhance process efficiency. Simulation software, such as Aspen Plus^®, provides a comprehensive platform for modeling extractive distillation operations, allowing for the integration of mass and energy balances, phase equilibrium relationships, and complex distillation configurations to systematically assess the feasibility of alternative solvents. Rigorous thermodynamic models, including the Non-Random Two-Liquid (NRTL) model in combination with the Redlich–Kwong equation of state, are frequently utilized to account for the non-ideal behavior of multi-component mixtures. These simulations not only enable the accurate forecasting of process performance but also assist in optimizing distillation column design, improving overall efficiency and reducing energy consumption. Several studies have employed computer-aided simulations to assess the effectiveness of conventional solvents like DMF and NMP in the extractive distillation of 1,3-butadiene from crude C₄ mixtures [4,9,10].

Given the health, safety, and environmental concerns associated with solvent emissions and losses, the transition towards green solvents has gained significant attention within the field of chemical engineering. Since the 1990s, green chemistry principles have emphasized the substitution of toxic solvents with non-toxic, renewable, and energy-efficient alternatives. The pursuit of greener solvents is closely linked to the broader goal of reducing hazardous chemical exposure while ensuring high-performance industrial operations. The use of green solvents aligns with the UN’s Sustainable Development Goals (SDGs). By adopting green solvents, industries support sustainable industrial development (SDG 9) and pollution reduction (SDG 13), thus contributing to a healthier environment (SDG 3), reduced chemical pollution (SDG 6), and responsible resource use (SDG 12). As regulatory restrictions tighten and sustainability efforts intensify, the search for innovative solvent solutions remains a fundamental challenge for the chemical industry.

Our previous work has highlighted 1,2-propylene carbonate—commonly referred to as propylene carbonate (PC)—as a promising green alternative for 1,3-butadiene extraction, presenting a viable substitute for traditional hazardous solvents [11]. Like DMF and NMP, PC is an aprotic dipolar solvent. Evaluations by Bello Forero et al. (2016) and the GSK Solvent Selection Guide have recognized propylene carbonate as a highly suitable and sustainable option with significant green chemistry benefits, positioning it as an ideal candidate for advancing environmentally friendly practices [12,13,14]. PC has been widely used in various industries, including electronics, pharmaceuticals, cosmetics, and carbon capture technologies, but its use in extractive distillation remains unexplored.

Although PC demonstrates slightly lower selectivity towards 1,3-butadiene (1.30 compared to 1.32 for DMF), it offers an effective balance between selectivity, cost-efficiency, and industrial feasibility. Furthermore, its environmentally friendly characteristics and improved health and safety profile align well with existing regulatory policies, including REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals), in the European Union. Additionally, CEFIC (European Chemical Industry Council) advocates for the development and adoption of safer and more sustainable chemical alternatives, further reinforcing the significance of exploring PC as a viable solvent for industrial applications.

This study aims to bridge the gap between conceptual green solvent selection and practical industrial implementation by assessing the feasibility of integrating propylene carbonate (PC) into an existing 1,3-butadiene plant with minimal process modifications. PC has not been extensively explored in this context, unlike more commonly studied solvents such as DMF and NMP; thus, this work offers new insights into its performance and potential for use in industrial-scale applications. To achieve this, a comparative analysis of using PC and DMF is conducted using Aspen Plus^® (v12.1) to assess both the operational and economic implications of transitioning to a more sustainable solvent. Additionally, this work examines potential enhancements in energy efficiency, process simplification, and overall sustainability benefits, aligning with the principles of green chemistry—specifically, principles 3, 5, and 6. The findings contribute to the broader initiative of developing sustainable chemical processes while ensuring industrial efficiency, without compromising performance.

1,3-Butadiene Process Overview

The 1,3-butadiene extraction process is a multi-stage procedure that consists of several interconnected distillation sections, including two extractive distillation columns, a conventional distillation unit, and a solvent purification section (see Figure 1). These units work synergistically to achieve efficient 1,3-butadiene separation while facilitating solvent recovery and reuse. Process optimization techniques, such as heat integration strategies, further enhance the sustainability of the operation by minimizing total energy consumption. For instance, the hot solvent stream from the bottom of the stripping columns (T2 and T5) is redirected to a heat recovery circuit, where it is utilized to preheat subsequent distillation column streams, reducing external energy input requirements.

In the first extractive distillation unit (T1), the C₄ feed stream is introduced into the primary distillation column, where lighter hydrocarbons with higher relative volatility, such as 1-butene, are removed as a raffinate stream. The bottom stream, enriched with 1,3-butadiene and other less volatile hydrocarbons, is directed towards a stripping unit (T2) for further purification. The stripping process effectively separates hydrocarbons and impurities from the solvent, producing an intermediate stream that undergoes additional refinement in a second extractive distillation column (T3). In this step, residual low-volatility impurities are eliminated, yielding high-purity 1,3-butadiene as the final overhead product.

The conventional distillation section serves to refine valuable by-products such as methylacetylene (propyne) and cis-2-butene. Additives, such as tert-butyl catechol (known as TBC) and silicone-based inhibitors, are commonly employed to suppress 1,3-butadiene polymerization during the distillation process. However, the complex polymerization mechanisms of butadienes (1,2-butadiene and 1,3-butadiene), including their potential for “popcorn” polymerization under specific reaction conditions, present operational challenges that must be managed carefully. Yet, this phenomenon did not seem to be essential for the execution of this work. Future research should consider incorporating polymerization kinetics into simulation studies to develop more comprehensive process models.

The final stage of the process involves solvent purification, where a portion of the second stripper’s (T5) bottom stream is directed to a dedicated distillation column (T8) for solvent purification. The implementation of an efficient solvent regeneration system is critical for minimizing solvent losses, reducing operational costs, and enhancing the overall sustainability of the process.

Ensuring the accuracy of a simulation that aims to closely replicate real plant operation requires not only precise input data but also a robust thermodynamic framework capable of reliably predicting phase behavior under industrial conditions. A key aspect of this modeling process is the accurate representation of the phase equilibrium, which dictates the distribution of components between phases and directly influences separation efficiency. To obtain reliable results, selecting an appropriate thermodynamic model and accurately determining binary interaction parameters are essential. In extractive distillation, where a solvent is introduced to enhance separation, the most critical interactions to characterize are those between the components of the mixture and the solvent. However, due to the scarcity of experimental data and literature-reported interaction parameters for certain key component pairs, new vapor-liquid equilibrium (VLE) data may have to be generated using predictive methods, such as the Conductor-like Screening Model for Real Solvents (COSMO-RS)—a quantum-chemistry-based approach—that was used in this study. This methodology improves the predictive accuracy of the simulation by generating equilibrium data through COSMO-RS, which may then be used to calculate the missing interaction parameters and better capture the non-ideal behavior of the system.

2. Materials and Methods

A steady-state model was developed in the Aspen Plus^® framework for both DMF and PC solvents. The initial step involved modeling the DMF-based process using real data from an operational plant. The feed specifications and operating conditions utilized in the simulations were sourced directly from the plant to ensure the highest possible accuracy and alignment between the simulated results and actual industrial performance. Typically, the 1,3-butadiene content in C₄ mixtures received from naphtha crackers, as was the case in this plant, is between 40–55 wt%. The solvent used for extraction was 97.0 wt% pure and contains 3.0 wt% of TAR (simulated as n-eicosane, C₂₀H₄₂) and dimer (4-vinylcyclohexene).

Table 1. Typical composition of the fresh C₄ fraction and solvent.

C4 Mixture Components	Composition (wt%)
Propane	0.03
Propadiene	0.03
Methylacetylene	0.02
1,2-Butadiene	0.25
1,3-Butadiene	44.98
cis-2-Butene	4.26
trans-2-Butene	5.41
iso-Butene	20.72
1-Butene	13.67
n-Butane	4.98
iso-Butane	4.98
Ethylacetylene	0.09
Vinylacetylene	0.49
n-Pentane	0.09
DMF solvent	Composition (wt%)
DMF	97.00
TAR	2.50
Dimer	0.50

shows a typical composition of the hydrocarbon feed stream.

When experimental data for specific molecular pair interactions are unavailable, or when dealing with emerging compounds such as PC, for which limited studies have been published, estimating missing interaction parameters becomes necessary. Unlike UNIFAC, one of the most widely used thermodynamic models for predicting phase equilibria, which requires predefined binary interaction parameters for every molecular substructure, COSMO-RS provides a more flexible and predictive approach. COSMO-RS can estimate missing interaction parameters and predict phase behavior across a broader range of compounds, including those containing novel functional groups or lacking sufficient experimental thermodynamic data [15,16]. Moreover, UNIFAC has the drawback of not discriminating between isomers, such as 1,3-butadiene and 1,2-butadiene—a crucial aspect in this analysis [16].

In this study, both TURBOMOLE software (version 23.0.0 from BIOVIA TmoleX 2023, Vélizy-Villacoublay, France) and the COSMOtherm model (version 23.0.0 from BIOVIA COSMOtherm 2023) were employed within the COSMO-RS framework to model and predict molecular structures. The molecular structures were first geometrically optimized to generate the COSMO files, which were subsequently processed within the COSMOtherm package to obtain the equilibrium data and thermodynamic properties essential for an accurate simulation.

In addition to determining binary interaction parameters, understanding how components behave at different concentrations is fundamental for designing an efficient separation process. One of the most important thermodynamic properties influencing phase equilibrium calculations is the activity coefficient, which quantifies how much a liquid mixture deviates from ideal behavior. Particularly in extractive distillation, where a solvent is used to modify relative volatilities (α_ij), accurate activity coefficient predictions are crucial for determining the feasibility and efficiency of the process, as can be seen in Equation (1):

$${\alpha }_{ij}={\displaystyle \frac{\raisebox{1ex}{$y_i$}\!\left/ \!\raisebox{-1ex}{$x_i$}\right.}{\raisebox{1ex}{$y_j$}\!\left/ \!\raisebox{-1ex}{$x_j$}\right.}}\propto {\displaystyle \frac{{\gamma }_i}{{\gamma }_j}}$$

(1)

where i and j are the key components (i.e., the components that define the desired separation), y and x are the vapor and liquid molar fractions, respectively, and $\gamma$ is the activity coefficient of the liquid phase.

The NRTL (Non-Random Two-Liquid) model is widely used to describe such non-ideal systems, as it is a local composition model that accounts for molecular interactions through binary interaction parameters. This model is particularly effective in representing the phase behavior of polar and strongly interacting compounds, making it well-suited for the extractive distillation of 1,3-butadiene. Given the system dependency of thermodynamic models, as highlighted in previous studies, selecting an appropriate model is critical for reliable simulations. In this study, the NRTL model integrated with the Redlich–Kwong (RK) equation of state was chosen to enhance the predictive accuracy of phase equilibrium calculations, by also accounting for non-idealities in the vapor phase.

With the newly predicted VLE data from COSMO-RS, it was then possible to use the NRTL equation to compute the missing binary interaction parameter, b_ij, which is the key factor in describing the non-ideality of the liquid phase. In most cases, though not universally, the highest value of the activity coefficient is observed at infinite dilution, which represents the limiting behavior as the solute concentration approaches zero. As such, the infinite dilution activity coefficient (${\gamma }^{\infty }$) may serve as a useful parameter for assessing the effectiveness of a solvent in separation processes [17]. Given its significance in describing the thermodynamic behavior of solutions, ${\gamma }^{\infty }$ is widely recognized as a critical factor in evaluating separation efficiency and solvent suitability [18,19]. To this end, the binary interaction parameters were calculated using the values of the activity coefficients at infinite dilution predicted by the COSMO-RS model. For a binary liquid mixture of components i and j, the NRTL equation at infinity dilution is given by:

$$\ln {\gamma }_i^{\infty }={\tau }_{ji}+{\tau }_{ij}{e}^{-\alpha {\tau }_{ij}}$$

(2)

$$\mathit{ln}{\gamma }_j^{\infty }={\tau }_{ij}+{\tau }_{ji}{e}^{-\alpha {\tau }_{ji}}$$

(3)

where

$${\tau }_{ij}=a_{ij}+{\displaystyle \frac{b_{ij}}{T}}$$

(4)

$${\tau }_{ji}=a_{ji}+{\displaystyle \frac{b_{ji}}{T}}$$

(5)

The non-randomness parameter α in the NRTL model was set to 0.3 for this study, which falls within the commonly used range of 0.2 to 0.47 for many binary mixtures, particularly for hydrocarbon systems. This value was selected based on established practices used in the modeling of similar hydrocarbon mixtures [20]. The binary energetic terms ${\tau }_{ij}$ and ${\tau }_{ji}$ depend on the temperature (T). In this study, the temperature dependence (Equations (4) and (5)) was correlated, setting a_ij as equal to zero and obtaining the values of b_ij. Indeed, by using activity coefficients at infinite dilution, we can determine only two parameters (b_ij and b_ji), while all other parameters (a_ij, a_ji, and α) must be assigned fixed values.

The new binary interaction parameters (see

Table 2. Binary interaction parameters (b_ij) for the NRTL model.

i	j	bij (K)	bji (K)	i	j	bij (K)	bji (K)
Propane	DMF	a	a	Propane	PC	984.783	247.835
Propadiene	DMF	497.986	−388.861	Propadiene	PC	a	a
Methylacetylene	DMF	207.888	−419.219	Methylacetylene	PC	a	a
1,2-Butadiene	DMF	491.072	−269.549	1,2-Butadiene	PC	513.262	−1.737
1,3-Butadiene	DMF	b	b	1,3-Butadiene	PC	477.040	−42.052
cis-2-Butene	DMF	b	b	cis-2-Butene	PC	631.569	137.446
trans-2-Butene	DMF	556.141	−134.156	trans-2-Butene	PC	646.759	159.813
iso-Butene	DMF	b	b	iso-Butene	PC	599.408	118.921
1-Butene	DMF	b	b	1-Butene	PC	627.364	139.585
n-Butane	DMF	b	b	n-Butane	PC	909.085	390.750
iso-Butane	DMF	710.891	−9.294	iso-Butane	PC	899.663	367.293
Ethylacetylene	DMF	163.699	−343.302	Ethylacetylene	PC	214.759	−67.743
Vinylacetylene	DMF	7.664	7.664	Vinylacetylene	PC	−304.505	−304.505
n-Pentane	DMF	a	a	n-Pentane	PC	849.063	523.263
TAR	DMF	b	b	TAR	PC	758.290	2160.690
Dimer	DMF	385.433	−68.823	Dimer	PC	430.751	364.928
Water	DMF	b	b	Water	PC	1036.497	385.343

(a) Retrieved from the NISTV121 NIST-RK database via Aspen Plus^®, some of these pairs have values of the interaction parameter a_ij different from zero and non-randomness values, α, different from 0.3. (b) From the APV121 VLE-RK database, retrieved from Aspen Plus^®, some of these pairs have values of the interaction parameter a_ij different from zero and non-randomness values, α, different from 0.3.

) were then implemented into the Aspen Plus^® properties framework. For certain component pairs, empirical values were already available from literature databases (see Table 2), providing a well-established basis for thermodynamic modeling. These pre-existing parameters were incorporated into the simulation to maintain higher consistency with real data.

The next step was to assess the separation efficiency within the distillation columns. A crucial aspect of this evaluation involved determining the theoretical number of trays required to achieve the desired separation performance. The theoretical number of trays was determined by initially considering the actual number of trays in the distillation columns and systematically reducing them until significant differences in composition and temperature were observed between adjacent stages. This iterative approach ensured that only effective stages, which contribute meaningfully to the separation process, were included in the final design. If a stage exhibited negligible differences in temperature and composition when compared to adjacent stages, it was deemed ineffective contributing little to the separation process. Such stages were excluded from the final design as they added unnecessary complexity to the simulation. This approach is particularly critical in complex systems, such as the one studied here, which involves multiple recycles and interconnected equipment (see Figure 1). Reducing the number of ineffective stages helps to streamline the simulation, improving computational efficiency and model convergence. Moreover, removing non-contributing stages minimizes the potential for numerical instability, which is often a concern in simulations with extensive recycle loops and highly coupled systems. By ensuring that only effective stages were included, the simulation more accurately represented the operational behavior of the distillation columns, leading to more reliable results.

3. Results and Discussion

3.1. Validation of the Simulation Using DMF Solvent

To ensure the reliability of the simulation framework, the first step involved validating the DMF-based extractive distillation process against real plant data. The operational parameters, including solvent-to-feed ratios, distillation column conditions, and energy requirements, were directly implemented from an existing industrial facility. However, due to confidentiality agreements, the exact industrial data used for validation cannot be disclosed. Instead, the model was calibrated using representative values that closely reflect real operational conditions. The simulated results demonstrated strong agreement with industrial performance benchmarks, confirming that the model accurately predicts the separation efficiency, solvent recovery, and energy consumption of the 1,3-butadiene process. This validation provided confidence in extending the model to evaluate the performance of PC as an alternative solvent.

3.2. VLE Behavior of PC vs. DMF

The newly determined binary interaction parameters derived using COSMO-RS and the NRTL model were incorporated into Aspen Plus^® to refine the phase equilibrium representation for both DMF and PC-based systems. Table 2 shows the binary interaction parameters used for both DMF and PC simulations.

After validating the DMF-based process, a new simulation was conducted using PC as a solvent while maintaining the same operational feed rates and process conditions. The objective was to introduce PC with minimal modifications to the existing process configuration, ensuring a direct comparison between the two solvents. This approach enabled a comprehensive assessment of PC’s impact on process performance while preserving the original industrial framework as much as possible.

The simulation results indicated that the overall behavior of the PC-based process closely resembled that of the DMF system, confirming its feasibility as an alternative solvent. Figure 2 presents the calculated temperature profile and the 1,3-butadiene mass composition in the liquid for the first extractive distillation column (T1 in Figure 1). As expected, the temperature changes most rapidly at the top and bottom of the column and in the vicinity of the solvent feeding point and the C₄ fraction feeding point for the extractive distillation. Due to having a lower selectivity than DMF, PC simulation needed a solvent to feed ratio higher than DMF (6.8:1.0 for DMF and 7.6:1.0 for PC). Nonetheless, PC exhibited a comparable affinity for 1,3-butadiene separation, leading to a similar operational behavior. The process profiles for both solvents closely aligned, indicating that the overall separation dynamics remained consistent despite the change in solvent.

Another significant change observed in the simulation was the insolubility of methylacetylene in PC, rendering column T6 ineffective. As a result, this column was no longer necessary in the process configuration. The elimination of T6 presents an opportunity for energy savings, as it reduces both the operational complexity and utility requirements of the system. The extent of these energy savings and their impact on overall process efficiency will be discussed in detail in the following section. This simplified the process design, as methylacetylene was effectively removed in the raffinate stream from the first extractive distillation. This modification alone resulted in a 0.4% reduction in utility costs, primarily due to an estimated savings of 43 tonnes per hour in cooling water consumption, thus reducing the use of resources.

A critical challenge associated with PC as a solvent is its high normal boiling point (242 °C) compared to DMF (153 °C). This requires operating at lower pressures (as low as −0.8 barg) to accommodate the thermal limitations of existing equipment. However, not all distillation columns in the process can operate under vacuum conditions. Specifically, the first stripper, responsible for separating the solvent from 1,3-butadiene and acetylene compounds, cannot operate at reduced pressures due to the operational constraints imposed by the positive displacement compressor downstream. In contrast, the second stripper (T5 in Figure 1) is capable of operating under these conditions, allowing for partial adaptation of the process to the use of PC.

Additionally, a conventional DMF-based process primarily relies on low-pressure (LP, 5 barg) and medium-pressure (MP, 12 barg) steam for heating. However, the implementation of PC required a higher hot utility, which was successfully met by employing higher medium-pressure steam (MP+, 18 barg). This adaptation was critical to avoid the use of high-pressure steam, thereby minimizing infrastructural modifications and maintaining the feasibility of the process. Additionally, PC showed similar behavior to DMF within the general solvent circuit, where hot solvent from the bottom of the strippers was used to heat other column streams and equipment, as shown in Figure 1 (blue line), leading to energy conservation.

Table 3. Simulation operational conditions using DMF and PC as a solvent.

Equipment	Parameter	DMF	PC
-	C4 fraction feed rate (tonne/h)	10.07	10.07
T1	Solvent/C4 feed	6.75	7.55
T1	Reflux/Solvent	0.12	0.14
T2	Reflux/Solvent to T1	0.06	0.06
-	1,3-Butadiene rate in C4 + Recycle stream of T4 (tonne/h)	5.03	4.72
T3	Solvent/Feed	1.90	1.99
T3	Reflux/Solvent	0.53	0.53
T5	Reflux/Solvent to T3	0.12	0.06
T6	Reflux/Feed	1.02	-
T7	Reflux/Feed of T6	4.87	4.67
T8	Reflux/Feed	0.43	0.35

highlights the key operational rates and ratios for the DMF and PC simulations, providing insight into their comparative performance within the same process framework. The data indicates that PC solvent values align closely with those of DMF, confirming that PC integrates seamlessly into the existing process configuration with minimal adjustments. Notable differences include a slightly higher solvent-to-C₄ feed ratio for PC, which compensates for its lower selectivity relative to DMF. Furthermore, the reflux-to-feed ratio in the downstream columns (T5 and T8) is lower for PC, contributing to further reduced energy consumption in some sections of the plant.

Overall, the transition to PC as a solvent demonstrated high compatibility with the existing 1,3-butadiene plant configuration, achieving the desired separation performance (1,3-butadiene product 99.6 wt%) with minimal process modifications. In terms of utility consumption, PC required slightly higher electricity usage due to its higher solvent-to-feed ratio compared to DMF (Figure 3). However, the increased electricity costs associated with PC can be offset in the future by the plant’s transition towards self-sufficiency in renewable energy production, thereby enhancing the long-term sustainability and economic viability of the process. Nonetheless, despite using higher pressure steam, the total heat consumption in the PC simulation was 3.7% lower than in the DMF process. On the other hand, due to having higher solvent-to-feed ratios, the electricity consumption was slightly higher, around 7.9%. To put the results into perspective, Aspen Plus^® also provides estimated cost consumption rates. According to the simulation results, the process using PC solvent incurs into 2.7% higher operating costs compared to DMF.

The total capital cost estimates provided by Aspen Plus^® reveal that the process using PC requires approximately 4.7% less capital investment compared to the traditional DMF-based system. The estimated total capital cost for the DMF process amounts to 21.2 M EUR, whereas for PC, it is 20.2 M EUR, reflecting a significant reduction. This decrease is primarily attributed to the elimination of column T6 and its associated equipment, which was rendered unnecessary. The exclusion of column T6 further improves the overall solvent circuit by optimizing heat distribution among the remaining columns, as shown in Figure 1. With the heat previously allocated to column T6 now available for reuse, it can be redirected to other columns, reducing the required vapor and, consequently, lowering overall heat consumption. A cost analysis was conducted to assess the financial impact of solvent substitution at an industrial scale, building upon the findings of a previous study [11]. The study reported an average acquisition cost of 1.1 EUR/kg for PC and 1.4 EUR/kg for DMF. Given that an average 1,3-butadiene extraction plant requires approximately 150 tonnes of solvent to operate at full capacity, replacing DMF with PC would result in a 21.4% reduction in solvent stock costs. Additionally, while the simulation using PC required a higher solvent-to-feed ratio, its overall makeup cost is approximately 12.2% lower, contributing to a potential reduction in long-term operational expenses. This reduction is particularly significant when considering long-term operational expenses and the potential for further cost optimization through solvent recovery and recycling strategies. Moreover, the lower cost of PC, when considered in conjunction with its enhanced environmental and safety profile, serves to further bolster its viability as a sustainable alternative for 1,3-butadiene extractive distillation.

However, before transitioning to the actual industrial process, an experimental program must be conducted to validate the conclusions drawn from our work, which were primarily based on predictive methods.

4. Conclusions

This study has demonstrated the feasibility of replacing N,N-dimethylformamide with propylene carbonate, which is a greener solvent for 1,3-butadiene extraction, leveraging advanced thermodynamic modeling and process simulations. The transition to PC aligns with REACH regulations and CEFIC sustainability recommendations, offering an environmentally responsible alternative while maintaining industrial performance. Thus, this work is an effective contribution to the development of more sustainable industrial processes.

The Aspen Plus^® simulations demonstrated that PC attains a separation efficiency comparable to that of DMF with minimal modifications to the existing process. However, due to PC’s lower selectivity towards 1,3-butadiene, a higher solvent-to-feed ratio was necessary, resulting in a moderate increase in operational costs. However, the elimination of column T6, due to the insolubility of methylacetylene in PC, led to capital cost reductions of approximately 4.7%, enhancing the overall economic attractiveness of this solvent transition. Furthermore, a cost analysis revealed that PC has a 21.4% lower solvent procurement cost and 12.2% lower make up cost compared to DMF, reinforcing its economic viability, particularly when considering long-term operational expenses and potential cost optimizations through solvent recovery and recycling strategies.

An analysis of energy consumption revealed that, while PC required 7.9% more electricity, overall heat consumption was reduced by 3.7%. Additionally, cost estimations from Aspen Plus^® indicated that operating expenses were 2.7% higher for PC, suggesting that further optimization efforts could help bridge the cost gap. However, targeted process improvements, such as enhanced heat integration, optimized column configurations, and refined solvent recovery strategies, could reduce additional operational costs while maintaining the environmental benefits of PC.

Despite these differences, PC was found to be highly compatible with existing industrial configurations, indicating its strong potential as a sustainable alternative to conventional hazardous solvents. The elimination of hazardous solvent emissions, compliance with green chemistry principles, reductions in capital costs, and lower solvent acquisition costs reinforce the viability of PC in 1,3-butadiene extractive distillation, leading to a more sustainable process. However, the implementation of PC as a solvent in an existing plant requires adjustments due to its higher boiling point compared to DMF. Specifically, the use of MP vapor at higher pressures (18 bar g for PC compared to 12 bar g for DMF) will be necessary to achieve the desired operating conditions. Special attention should also be given to the increased temperatures, as some equipment in the existing plant may not be designed to withstand these higher conditions, requiring potential upgrades or modifications to ensure safe and efficient operation.

This study predominantly relies on simulation results, and while these provide valuable insights, further experimental work would offer a more robust validation of PC’s performance in 1,3-butadiene extractive distillation. Experimental data would allow for a more direct and accurate comparison between PC and DMF, improving the reliability of the performance evaluation and ensuring the practical feasibility of PC as a solvent in actual applications. We aim to pursue this experimental validation in future work to confirm our simulation findings and comprehensively assess the potential of PC in industrial settings.