Assessing the Environmental Impacts of the Valorization of Creole-Antillean Avocado via an Extractive-Based Biorefinery in the Montes de María Region

Abstract

In recent years, the environmental evaluation of biorefineries has become critical for ensuring sustainable practices in bio-based production systems. This study focuses on the application of the Waste Reduction (WAR) Algorithm to assess the environmental impacts of an Extractive-based Creole-Antillean Avocado Biorefinery located in Northern Colombia, aimed at producing bio-oil, chlorophyll, and biopesticide from avocado pulp, peel, and seed, respectively. The environmental impacts were evaluated using the WAR algorithm, which quantifies the potential environmental impacts (PEI) of different process streams. The following four scenarios were developed: (1) considering only waste, (2) including waste and products, (3) including waste and energy sources, and (4) incorporating waste, products, and energy consumption. This study analyzed global impacts focusing on atmospheric and toxicological categories, with a detailed assessment of the most critical scenario. The results indicated that Scenario 4 had the highest PEI, particularly in the atmospheric and toxicological categories, driven by emissions of volatile organic compounds (VOCs), greenhouse gases (GHGs), and the presence of heavy metals. However, the avocado biorefinery process demonstrated a net reduction in overall environmental impacts, with negative PEI generation rates across all scenarios, suggesting that the biorefinery transforms high-impact substances into products with lower global impact potential. Energy consumption emerged as a significant contributor to environmental impacts, particularly in acidification potential (AP) and Atmospheric Toxicity Potential (ATP). Using natural gas as an energy source had a relatively lower environmental impact compared to coal and liquid fuels, emphasizing the need to optimize energy use in biorefinery design to improve environmental performance.

1. Introduction

The increasing emphasis on sustainability within industrial processes has placed biorefineries at the forefront of efforts to transition from fossil-based to bio-based economies. As the global community faces the dual challenges of depleting fossil fuel reserves and escalating environmental concerns, biorefineries offer a promising solution by utilizing renewable biomass to produce energy, chemicals, and materials. The concept of a biorefinery, analogous to a conventional petroleum refinery, involves the conversion of biomass into multiple value-added products through various biochemical, thermochemical, and mechanical processes [1]. This not only enhances resource efficiency but also aligns with the principles of the circular economy, which seeks to minimize waste and make the most of available resources [2]. Among the various types of biomasses available for biorefining, avocado has emerged as a particularly valuable feedstock due to its high oil content, nutritional value, and the potential for utilizing its by-products, such as pulp, peel, and seeds [3]. The unique composition of avocado [4], especially its fatty acids and bioactive compounds, makes it an ideal candidate for producing bio-oil, biopesticides, and other bioproducts, thereby contributing to the economic viability of biorefineries.

In Colombia, the avocado industry has expanded rapidly, particularly in regions like Montes de María, leading to significant quantities of agricultural waste [5]. The country has become one of the leading exporters of avocados globally, with the Hass variety being particularly popular in international markets [6]. However, this growth in avocado production has also led to the accumulation of large amounts of waste, primarily from pulp, peel, and seeds, which are often discarded or underutilized. Due to the fact that during the stages of harvesting, transportation, and selection, a percentage of the fruit is discarded for not meeting weight requirements or suffering damage during handling, this non-exportable avocado, which would otherwise be underutilized or discarded [7,8]. This waste, if left untreated, can pose significant environmental challenges, including the release of greenhouse gases during decomposition and the potential contamination of soil and water resources. However, these by-products also represent a valuable resource for biorefining, as they contain a range of bioactive compounds, oils, and other valuable materials that can be converted into high-value products [9]. By converting avocado waste into bio-oil, chlorophyll, and biopesticides, the environmental and economic sustainability of the avocado supply chain can be greatly enhanced. Moreover, the valorization of these by-products aligns with global sustainability goals, contributing to waste reduction, energy efficiency, and the production of renewable resources.

Despite the technical feasibility of biorefining avocado waste, a thorough environmental evaluation is necessary to ensure that these processes contribute positively to sustainability goals. While the economic and technical aspects of biorefineries have been studied, the environmental impacts of these processes are equally important and require careful assessment. Environmental evaluations are essential for identifying potential negative impacts, optimizing process designs, and ensuring that biorefineries operate within sustainable limits. In this context, several tools and methodologies have been developed to assess the environmental performance of industrial processes, including Life Cycle Assessment (LCA), the Tool for the Reduction and Assessment of Chemical and other Environmental Impacts (TRACI), and the Waste Reduction (WAR) Algorithm [10,11]. Each of these tools offers unique insights into the environmental implications of biorefinery processes, from resource extraction to end-of-life disposal [12]. For instance, LCA provides a comprehensive analysis of environmental impacts across the entire life cycle of a product or process, from raw material extraction to disposal [13]. TRACI, on the other hand, focuses on specific impact categories such as global warming potential, acidification, and human toxicity, offering a detailed analysis of emissions and resource use [14].

Among these tools, the WAR algorithm is particularly suited for evaluating the potential environmental impacts (PEI) of emissions and process streams, making it a valuable tool for identifying and mitigating the negative environmental impacts of biorefineries [15]. The WAR algorithm, developed by the U.S. Environmental Protection Agency (EPA), assesses environmental impacts across multiple categories, including global warming potential, ozone depletion, acidification, and toxicity [16]. Unlike other tools that may require extensive data collection and complex modeling, the WAR algorithm is designed to be integrated directly into process design and simulation tools, allowing for real-time assessment of environmental impacts during the development and optimization of industrial processes. This feature makes the WAR algorithm particularly useful for the early-stage design of biorefineries, where it can be used to evaluate different process configurations and identify the most sustainable options before significant investments are made [17].

The application of the WAR algorithm to biorefinery processes allows for a detailed assessment of the environmental impacts associated with different stages of the production process, from raw material preparation to product refinement and waste management [15]. By quantifying the PEI of various emissions and process streams, the WAR algorithm enables the identification of key environmental stressors and the formulation of strategies to mitigate these impacts. This not only helps in reducing the overall environmental footprint of the biorefinery but also contributes to the development of more sustainable industrial practices [18]. In the case of avocado biorefineries, the application of the WAR algorithm is particularly relevant given the complex nature of the feedstock and the multiple product streams involved. By evaluating the environmental impacts of each process step, from the extraction of bio-oil from avocado pulp to the production of biopesticides from the seeds, the WAR algorithm provides a comprehensive picture of the environmental performance of the biorefinery and highlights areas where improvements can be made.

This study focuses on applying the WAR algorithm to assess the environmental impacts of an Extractive-based Creole-Antillean Avocado Biorefinery in the region of Montes de María, located in Northern Colombia. The Extractive-based biorefinery under consideration processes avocado pulp, peel, and seeds to produce bio-oil, chlorophyll, and biopesticides, respectively. The analysis was conducted across four scenarios, each incorporating different combinations of waste, products, and energy sources, to provide a comprehensive assessment of the biorefinery’s environmental performance. The scenarios were designed to reflect different operational conditions and to identify the most sustainable configuration for the biorefinery. The first scenario considered only the waste generated by the process, while the second scenario included both waste and product streams. The third scenario incorporated the environmental impacts of the energy sources used in the process, focusing on the impacts of waste and energy while omitting product effects. The fourth scenario included all components—waste, products, and energy consumption—providing a complete view of the environmental impacts associated with the biorefinery.

The objectives of this research are threefold: (1) to evaluate the environmental impacts of the Extractive-based Creole-Antillean Avocado Biorefinery using the WAR algorithm, (2) to identify the primary contributors to these impacts, and (3) to propose strategies for optimizing the biorefinery design to enhance its sustainability. The results of this study will provide valuable insights into the environmental performance of the Extractive-based Creole-Antillean Avocado Biorefinery and contribute to the broader discourse on sustainable biorefining practices. By addressing these objectives, the study aims to contribute to the growing body of knowledge on the environmental performance of biorefineries and to support the development of more sustainable bio-based industries.

The significance of this study lies in its potential to inform policy and industrial practices, ensuring that biorefineries contribute to a more sustainable and environmentally responsible future. As the demand for bio-based products continues to grow, the need for rigorous environmental assessments becomes increasingly important. This study provides a framework for such assessments, demonstrating the utility of the WAR algorithm in identifying and mitigating environmental impacts in biorefinery processes. Moreover, the insights gained from this study can be applied not only to the avocado industry but also to other agricultural waste valorization efforts, contributing to the development of more sustainable and environmentally friendly industrial practices.

2. Materials and Methods

2.1. Process Description

The process under study focuses on the valorization of Creole-Antillean avocado (Persea americana) waste through a simulated Extractive-based Creole-Antillean Avocado Biorefinery, aimed at producing bio-oil, chlorophyll, and biopesticides. This process was modeled using Aspen Plus^® software (Version 12.1), a tool well suited for simulating chemical processes due to its robust thermodynamic modeling capabilities. The choice of the thermodynamic model is crucial for ensuring accurate simulation of the process, particularly when dealing with non-ideal liquid mixtures and the gas phase behavior of substances involved. For the liquid phase, the Non-random Two-liquid (NRTL) model was selected due to its effectiveness in handling non-electrolytic polar substances, which is critical for modeling the behavior of components like bio-oils, chlorophyll, and biopesticides. Compounds extracted from avocado waste, the substances considered for the composition of raw materials and products, are presented in

Table 1. Material balance of the bio-oil extraction process from Creole-Antillean avocado pulp, part 1.

Stream Number	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
Temperature (°C)	25	25	25	25	25	25	25	25	25	25	25	25	25	25	25	25
Pressure (bar)	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Mass flows (kg/h)	1102	2241	1243	2100	1040	203	747	293	103	191	205	435	511	127	702	593
Mass Fractions
Sodium hypochlorite	-	2.0 × 10−4	3.6 × 10−6	2.1 × 10−4	-	2.2 × 10−5	-	-	-	-	-	-	8.8 × 10−6	-	6.4 × 10−6	7.1 × 10−6
Water	0.66	0.99	0.71	0.99	0.77	0.38	0.88	0.49	1	0.74	0.52	1	0.94	0.24	0.89	0.99
Leucine	0.01	-	0.01	-	0.01	0.02	0.01	0.03	-	0.02	0.02	-	-	0.02	0.01	0
Glucose	0.18	-	0.16	-	0.11	0.43	0.06	0.24	-	0.01	0.32	-	0	0.67	0.01	0
Calcium oxide	0.03	-	0.01	0.01	0.02	0.01	0.01	0.03	-	0	0.05	-	6.7 × 10−4	0.01	1.0 × 10−3	0
Lauric acid	2.4 × 10−5	-	2.1 × 10−5	-	2.0 × 10−5	2.6 × 10−5	1.0 × 10−5	4.4 × 10−5	-	6.8 × 10−5	-	-	1.0 × 10−5	-	2.6 × 10−5	1.7 × 10−5
Myristic acid	2.1 × 10−4	-	1.9 × 10−4	-	1.8 × 10−4	2.3 × 10−4	9.4 × 10−5	4.0 × 10−4	-	6.1 × 10−4	-	-	9.1 × 10−5	-	2.3 × 10−4	1.6 × 10−5
Pentadecanoic acid	2.4 × 10−5	-	2.1 × 10−5	-	2.0 × 10−5	2.6 × 10−5	1.0 × 10−5	4.4 × 10−5	-	6.8 × 10−5	-	-	1.0 × 10−5	-	2.6 × 10−5	1.7 × 10−6
Palmitic acid	0.04	-	0.04	-	0.03	0.04	0.02	0.08	-	0.12	-	-	0.02	-	0.05	3.0 × 10−3
Heptadecanoic acid	4.7 × 10−5	-	4.2 × 10−5	-	4.0 × 10−5	5.1 × 10−5	2.1 × 10−5	8.8 × 10−5	-	1.4 × 10−4	-	-	2.0 × 10−5	-	5.2 × 10−5	3.5 × 10−6
Stearic acid	1.3 × 10−3	-	1.1 × 10−3	-	1.1 × 10−3	1.4 × 10−3	5.7 × 10−4	2.4 × 10−3	-	3.7 × 10−3	-	-	5.6 × 10−4	-	1.4 × 10−3	9.5 × 10−5
Oleic acid	0.05	-	0.04	-	0.03	0.07	0.02	0.08	-	0.1	0.02	-	0.02	0.03	0.04	2.9 × 10−3
Linoleic acid	0.02	-	0.02	-	0.02	0.02	0.01	0.04	-	-	0.06	-	0.01	-	0.01	4.5 × 10−4
Linolenic acid	3.2 × 10−3	-	2.9 × 10−3	-	2.7 × 10−3	3.5 × 10−3	1.4 × 10−3	0.01	-	-	0.01	-	1.4 × 10−3	-	1.0 × 10−3	6.8 × 10−5
Arachidic acid	6.4 × 10−4	-	5.6 × 10−4	-	5.4 × 10−4	6.9 × 10−4	2.8 × 10−4	1.2 × 10−3	-	-	1.7 × 10−3	-	2.7 × 10−4	-	2.0 × 10−4	1.3 × 10−5
Isophytol	2.7 × 10−3	-	2.4 × 10−3	-	-	0.01	-	-	-	-	-	-	-	0.02	-	-
Tannins	2.6 × 10−3	-	2.3 × 10−3	-	2.8 × 10−3	-	1.5 × 10−3	0.01	-	-	0.01	-	-	-	-	-
Flavonoids	2.1 × 10−4	-	1.8 × 10−4	-	2.2 × 10−4	-	1.1 × 10−4	4.8 × 10−4	-	-	6.9 × 10−4	-	-	-	-	-
Phenols	9.9 × 10−4	-	8.8 × 10−4	-	1.1 × 10−2	-	5.5 × 10−4	2.3 × 10−3	-	-	3.3 × 10−3	-	-	-	-	-

,

Table 2. Material balance of the bio-oil extraction process from Creole-Antillean avocado pulp, part 2.

Stream Number	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32
Temperature (°C)	25	25	25	70	70	25	24	24	24	70	18	70	70	70	30	25
Pressure (bar)	1	1	1	0.3	0.3	1	0.3	0.3	0.3	1	1	1	1	1	1	1
Mass flows (kg/h)	108	855	855	689	167	32	199	13	186	151	151	35	1	34	34	32
Mass Fractions
Hexane	-	-	-	-	-	1.00	0.16	2.4 × 10−3	0.17	-	-	0.92	0.92	0.92	0.92	1.0
Sodium hypochlorite	2.3 × 10−6	2.9 × 10−7	2.9 × 10−7	-	1.4 × 10−6	-	1.2 × 10−6	1.87 × 10−8	1.3 × 10−6	1.6 × 10−6	1.6 × 10−6	-	-	-	-	-
Water	0.32	0.81	0.81	1.00	0.02	-	0.01	2.1 × 10−4	0.01	5.7 × 10−6	5.7 × 10−6	0.08	0.08	0.08	0.08	-
Leucine	0.03	0.01	0.01	-	0.05	-	0.04	0.05	0.04	0.05	0.05	-	-	-	-	-
Glucose	0.04	0.05	0.05	-	0.28	-	0.23	0.28	0.23	0.28	0.28	-	-	-	-	-
Calcium oxide	0.01	0.01	0.01	-	0.04	-	0.03	0.04	0.03	0.04	0.04	-	-	-	-	-
Lauric acid	1.6 × 10−4	2.9 × 10−5	2.9 × 10−5	-	1.4 × 10−4	-	1.2 × 10−4	1.5 × 10−4	1.2 × 10−4	1.5 × 10−4	1.5 × 10−4	-	-	-	-	-
Myristic acid	1.4 × 10−3	2.6 × 10−4	2.6 × 10−4	-	1.3 × 10−3	-	1.1 × 10−3	1.3 × 10−3	1.1 × 10−3	1.3 × 10−3	1.3 × 10−3	-	-	-	-	-
Pentadecanoic acid	1.6 × 10−4	2.9 × 10−5	2.9 × 10−5	-	1.4 × 10−4	-	1.2 × 10−4	1.5 × 10−4	1.2 × 10−4	1.5 × 10−4	1.5 × 10−4	-	-	-	-	-
Palmitic acid	0.28	0.05	0.05	-	0.26	-	0.22	0.27	0.22	0.27	0.27	-	-	-	-	-
Heptadecanoic acid	3.2 × 10−4	5.8 × 10−5	5.8 × 10−5	-	2.9 × 10−4	-	2.5 × 10−4	3.0 × 10−4	2.4 × 10−4	3.0 × 10−4	3.0 × 10−4	-	-	-	-	-
Stearic acid	0.01	1.6 × 10−3	1.6 × 10−3	-	0.01	-	0.01	0.01	0.01	0.01	0.01	-	-	-	-	-
Oleic acid	0.27	0.05	0.05	-	0.25	-	0.21	0.26	0.21	0.26	0.26	-	-	-	-	-
Linoleic acid	0.04	0.01	0.01	-	0.07	-	0.06	0.07	0.06	0.07	0.07	-	-	-	-	-
Linolenic acid	0.01	2.0 × 10−3	2.0 × 10−3	-	0.01	-	0.01	0.01	0.01	0.01	0.01	-	-	-	-	-
Arachidic acid	1.2 × 10−3	4.0 × 10−4	4.0 × 10−4	-	2.0 × 10−3	-	1.7 × 10−3	2.0 × 10−3	1.7 × 10−3	2.0 × 10−3	2.0 × 10−3	-	-	-	-	-
Tannins	-	1.3 × 10−3	1.3 × 10−3	-	0.01	-	0.01	0.01	0.01	0.01	0.01	-	-	-	-	-
Flavonoids	-	1.0 × 10−4	1.0 × 10−4	-	5.1 × 10−4	-	4.3 × 10−4	5.2 × 10−4	4.2 × 10−4	5.2 × 10−4	5.2 × 10−4	-	-	-	-	-
Phenols	-	4.8 × 10−4	4.8 × 10−4	-	2.4 × 10−3	-	2.0 × 10−3	2.5 × 10−3	2.0 × 10−3	2.5 × 10−3	2.5 × 10−3	-	-	-	-	-

,

Table 3. Material balance of the chlorophyll production process from Creole-Antillean avocado peel.

Stream Number	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48
Temperature (°C)	50	50	50	34	33	33	33	33	33	33	40	40	40	25	35	40
Pressure (bar)	0.1	0.1	0.1	1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	1	1	0.1
Mass flows (kg/h)	25	102	102	255	357	41	106	316	65	251	248	25	223	32.1	223	3
Mass Fractions
Water	1.0	0.06	0.06	0.14	0.11	0.05	0.02	0.12	-	0.15	0.16	0.16	0.16	-	0.16	0.03
Leucine	-	0.03	0.03	-	0.01	0.02	0.03	0.01	0.03	-	-	-	-	-	-	-
Glucose	-	0.83	0.83	-	0.24	0.62	0.80	0.19	0.91	-	-	-	-	-	-	-
Calcium oxide	-	0.01	0.01	-	4.2 × 10−3	0.01	0.01	3.3 × 10−3	0.02	-	-	-	-	-	-	-
Oleic acid	-	0.03	0.03	-	0.01	0.02	0.03	0.01	0.04	-	-	-	-	-	-	-
Ethanol	-	-	-	0.86	0.62	0.27	0.10	0.66	-	0.83	0.84	0.84	0.84	1.0	0.84	0.07
Isophytol	-	0.03	0.03	6.3 × 10−5	0.01	3.6 × 10−3	1.4 × 10−3	0.01	-	0.01	7.2 × 10−5	7.2 × 10−5	7.2 × 10−5	-	7.2 × 10−5	0.90

and

Table 4. Material balance of the biopesticide production process from Creole-Antillean avocado seeds.

Stream Number	49	50	51	52	53	54	55	56	57	58	59	60	61	62
Temperature (°C)	25.0	70.0	42.5	23.7	23.7	73.0	73.0	73.0	81.0	81.0	81.0	70.0	77.9	81.0
Pressure (bar)	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
Mass flows (kg/h)	205.0	912.6	923.1	194.5	194.5	441.1	26.7	414.5	230.5	207.5	23.1	223.6	246.7	183.9
Mass Fractions
Sodium hypochlorite	0.52	2.0 × 10−3	0.01	0.49	0.49	0.23	0.08	0.24	0.22	0.22	0.22	-	0.02	0.26
Water	0.02	-	-	0.02	0.02	0.01	0.03	0.01	-	-	-	-	-	0.02
Leucine	0.32	-	-	0.34	0.34	0.15	0.50	0.13	-	-	-	-	-	0.29
Glucose	0.05	-	-	0.05	0.05	0.02	0.07	0.02	-	-	-	-	-	0.04
Calcium oxide	0.02	-	-	0.02	0.02	0.01	0.02	0.01	-	-	-	-	-	0.01
Oleic acid	0.06	-	-	0.06	0.06	0.03	0.09	0.02	-	-	-	-	-	0.05
Linoleic acid	0.01	-	-	0.01	0.01	4.0 × 10−3	0.01	3.4 × 10−3	-	-	-	-	-	0.01
Linolenic acid	1.7 × 10−3	-	-	1.8 × 10−3	1.8 × 10−3	7.9 × 10−4	2.6 × 10−3	6.7 × 10−4	-	-	-	-	-	1.5 × 10−3
Arachidic acid	-	-	-	-	-	0.55	0.18	0.57	0.78	0.78	0.78	1.00	0.98	0.30
Tannins	0.01	-	-	0.01	0.01	0.00	0.01	3.5 × 10−3	-	-	-	-	-	0.01
Flavonoids	6.9 × 10−4	-	-	7.3 × 10−4	7.3 × 10−4	3.2 × 10−4	1.1 × 10−3	2.7 × 10−4	-	-	-	-	-	6.2 × 10−4
Phenols	3.3 × 10−3	-	-	3.5 × 10−3	3.5 × 10−3	1.5 × 10−3	0.01	1.3 × 10−3	-	-	-	-	-	3.0 × 10−3
Air	-	0.99	0.99	-	-	-	-	-	-	-	-	-	-	-

. The NRTL model provides reliable predictions of activity coefficients, making it ideal for liquid–liquid phase equilibria where non-ideality is pronounced [19]. For the vapor phase, the Redlich–Kwong (RK) equation of state was chosen. This decision is based on the RK model’s balance between accuracy and simplicity for representing gases and vapors, particularly at moderate pressures and temperatures, which are typical in the biorefinery processes modeled here [19]. This combination of NRTL for the liquid phase and RK for the vapor phase ensures robust thermodynamic modeling for the biorefinery’s conditions, enabling accurate predictions of phase behavior and process performance. Additionally, NRTL-RK has worked well with a high accuracy percentage for similar biorefinery processes previously developed by the authors [15,20,21]. The biorefinery process, as illustrated in the block diagram in Figure 1, represents a specific design proposed for the extraction of targeted bioproducts. It consists of three main sections, each dedicated to the extraction of a specific product: bio-oil from avocado pulp, chlorophyll from avocado peel, and biopesticide from avocado seeds.

2.1.1. Bio-Oil Production from Avocado Pulp

The bio-oil production process begins with the washing of whole avocados to remove surface contaminants using a sodium hypochlorite solution. After washing, the avocados are subjected to a peeling process where the peel is separated and sent to a washing stage to remove any adhering pulp. This is followed by a pulping stage where the avocado pulp is separated from the seeds, resulting in two streams: one containing the pulp and the other containing the seeds. Similar to the peel, the seeds are washed to ensure all remaining pulp is removed, producing a water–pulp mixture that is later centrifuged. The centrifugation step separates the pulp from the water, resulting in a concentrated pulp stream and a water stream.

The concentrated pulp is then homogenized to form a uniform paste, which is subsequently dried at 70 °C to reduce its moisture content while preventing thermal degradation of the biomass. The dried pulp is then mixed with methanol in an extraction stage carried out at 70 °C, where the methanol serves as a solvent to extract the bio-oil from the pulp [22]. The resulting mixture from the solvent extraction consists of methanol, bio-oil, and the remaining pulp. This mixture undergoes centrifugation to separate the defatted pulp from the bio-oil and methanol mixture. The methanol is then distilled off at 70 °C, taking advantage of the difference in boiling points between methanol and the bio-oil. The condensed methanol is recirculated within the process, and the bio-oil is collected as the final product. The overall yield from processing 10,644 tons of avocado per year is approximately 1457.25 tons of bio-oil. Table 1 and Table 2 contain the composition and operating conditions of the bio-oil extraction process streams and stream 27 in Table 2 corresponds to the output of the bio-oil as a product.

2.1.2. Chlorophyll Production from Avocado Peel

The chlorophyll production process uses the clean avocado peel, which is first dried at 50 °C to remove excess moisture. This drying is conducted with an additional stream of air to facilitate the process. The dried peel is then ground to reduce its particle size, improving the efficiency of the subsequent extraction process. Acetone is used as the solvent for chlorophyll extraction, introduced into the process to extract the chlorophyll from the ground peel [23]. The solvent and chlorophyll mixture obtained from this extraction is centrifuged to separate the solid peel residue from the chlorophyll-rich acetone solution.

The acetone–chlorophyll solution is then subjected to evaporation at 40 °C to separate the acetone, which is recovered and recirculated back into the process after purification. The remaining product is a concentrated chlorophyll extract, which contains the desired chlorophyll compounds for further utilization. Table 3 presents the operating conditions, mass flow rates, and compositions of the correlates of the chlorophyll production process; stream 48 describes the output conditions of the extracted chlorophyll.

2.1.3. Biopesticide Production from Avocado Seeds

For biopesticide production, the avocado seeds, previously separated from the pulp and peel, are initially dried using hot air at 70 °C to reduce their moisture content. Following drying, the seeds are ground to increase the surface area, enhancing the contact between the seeds and the solvent used in the extraction process. Ethanol, at a concentration of 90% (v/v), is employed as the solvent in a 1:2 ratio of seed to ethanol [24]. This extraction process targets compounds within the seeds that have known pest-control properties. The substances that have been shown to exhibit insecticidal, antimicrobial, and antifungal properties, making them effective in controlling pests and protecting plants from diseases are polyphenolic compounds, flavonoids, tannins, and various fatty acids, which are included in product composition as can be seen in stream 62 of Table 4 [24].

The extract obtained from the solvent extraction undergoes a separation process at 81 °C and atmospheric pressure. This process leverages the boiling point differences between ethanol (78.4 °C) and water (100 °C) to concentrate the biopesticide compounds. The final product is a dry extract with potential biopesticide properties, suitable for agricultural applications. Table 4 shows the operating conditions, mass flow rates, and compositions of the correlates of the biopesticide production process, and stream 62 corresponds to the output of the bioinsecticide as a product of the extractive-based biorefinery.

2.2. Waste Reduction (WAR) Algorithm

WAR is an environmental assessment tool developed by the U.S. Environmental Protection Agency (EPA) that quantifies the potential environmental impacts (PEI) of chemical processes. Unlike traditional environmental impact assessments, which are often performed post-design, WAR is integrated during the design phase, allowing for the identification and mitigation of environmental impacts from the earliest stages of process development [25]. In this study, the WAR GUI 1.0 software was employed to evaluate the environmental performance of the simulated biorefinery process, focusing on the production of bio-oil, chlorophyll, and biopesticides from Creole-Antillean avocado waste.

The assessment involved calculating the PEI for various process streams, with particular emphasis on reducing emissions and waste generation. The WAR algorithm considers multiple categories of environmental impact, which are classified into atmospheric and toxicological categories. For the atmospheric categories, ozone depletion potential (ODP), global warming potential (GWP), photochemical oxidant potential (PCOP), and acidification potential (AP) were evaluated. These categories measure the ability of emissions to contribute to global environmental issues such as climate change, smog formation, and acid rain [15]. The respective equations of the atmospheric categories are shown in

Table 5. Equations of the atmospheric categories.

Category	Unit	Description	Equation	Consideration
HTPI	PEI/kg of product	Human toxicity potential by ingestion.	$\mathrm{HTPI} ={\displaystyle \frac{1}{{\mathrm{LD}}_{50}}}$	LD50 is the lethal dose value by ingestion that would kill 50% of a sample population of rats, given in mg chemical/kg rat.
HTPE	PEI/kg of product	Human toxicity potential by exposure.	$\mathrm{HPTE}={\displaystyle \frac{1}{\mathrm{TLV}}}$	The TLV is the maximum concentration of a substance in the air that a person can be safely exposed to for 8 h a day, 40 h a week, without adverse effects, measured in mg/m3.
ATP	PEI/kg of product	Aquatic toxicity potential.	$\mathrm{ATP}={\displaystyle \frac{1}{{\mathrm{LC}}_{50}}}$	LD50 is the lethal dose by ingestion that kills 50% of a sample population of Pimephales promelas (a fish species), measured in mg/L.
TTP	PEI/kg of product	Terrestrial toxicity potential.	$\mathrm{TTP}={\displaystyle \frac{1}{{\mathrm{LD}}_{50}}}$	LD50 is the lethal dose value by ingestion that would kill 50% of a sample population of rats, given in mg chemical/kg rat.

.

Regarding the toxicological categories, the focus is on the potential toxicity to humans and the environment. Human toxicity potential by ingestion (HTPI), human toxicity potential by exposure (HTPE), aquatic toxicity potential (ATP), and terrestrial toxicity potential (TTP) were evaluated [15].

Table 6. Equations of the toxicological categories.

Category	Unit	Description	Equation	Consideration
ODP	PEI/kg of product	Ozone depletion potential.	$\mathrm{ODP}={\displaystyle \frac{\mathsf{\delta }{\left[{\mathrm{O}}_3\right]}_{\mathrm{i}}}{\mathsf{\delta }[{\mathrm{O}}_3]\mathrm{CFC-11}}}{\mathrm{m}}_{\mathrm{i}}$	CFC-11 is the ratio of ozone depletion by a unit of gas $\left(\mathsf{\delta }{\left[{\mathrm{O}}_3\right]}_{\mathrm{i}}\right)$ $\left({\mathrm{m}}_{\mathrm{i}}\right)$ is the emitted gas mass in kg.
GWP	PEI/kg of product	Global warming potential.	$\mathrm{GWP}={\displaystyle \frac{{\int }_0^{\mathrm{t}}{\mathrm{a}}_{\mathrm{i}}{\mathrm{c}}_{\mathrm{i}}\left(\mathrm{t}\right)\mathrm{dt}}{{\int }_0^{\mathrm{t}}{\mathrm{a}}_{{\mathrm{CO}}_2}{\mathrm{c}}_{{\mathrm{CO}}_2}\left(\mathrm{t}\right)\mathrm{dt}}}{\mathrm{m}}_{\mathrm{i}}$	${\mathrm{a}}_{\mathrm{i}}$ is the heat absorption per unit of greenhouse gas i, ${\mathrm{a}}_{\mathrm{C}{\mathrm{O}}_{2 }}$ for carbon dioxide. ${\mathrm{c}}_{\mathrm{i}} \left(\mathrm{t}\right)$ and ${\mathrm{c}}_{\mathrm{C}{\mathrm{O}}_2} \left(\mathrm{t}\right)$ are the concentrations of gas i and CO2 at time t. t is the GWP evaluation period, usually 100 years, and ${\mathrm{m}}_{\mathrm{i}}$ is the gas mass in kilograms.
PCOP	PEI/kg of product	Photochemical oxidant potential.	$\mathrm{PCOP}={\displaystyle \frac{\frac{{\mathrm{a}}_{\mathrm{i}}}{{\mathrm{b}}_{\mathrm{i}}\left(\mathrm{t}\right)}}{\frac{{\mathrm{a}}_{{\mathrm{C}}_2{\mathrm{H}}_4}}{{\mathrm{b}}_{{\mathrm{C}}_2{\mathrm{H}}_4}\left(\mathrm{t}\right)}}}{\mathrm{m}}_{\mathrm{i}}$	${\mathrm{a}}_{\mathrm{i}}$ is the ozone concentration change from emitting volatile organic compound i, with ${\mathrm{a}}_{{\mathrm{C}}_2{\mathrm{H}}_4}$ specific to ethylene. ${\mathrm{b}}_{\mathrm{i}}\left(\mathrm{t}\right)$ and ${\mathrm{b}}_{{\mathrm{C}}_2{\mathrm{H}}_4}\left(\mathrm{t}\right)$ represent the integrated emissions of compound i and ethylene up to time t. t is time, and ${\mathrm{m}}_{\mathrm{i}}$ is the mass of emitted volatile organic compound in kilograms.
AP	PEI/kg of product	Acidification potential.	$\mathrm{AP}={\displaystyle \frac{\frac{{\mathrm{V}}_{\mathrm{i}}}{{\mathrm{M}}_{\mathrm{i}}}}{\frac{{\mathrm{V}}_{{\mathrm{S}0}_2}}{{\mathrm{M}}_{{\mathrm{S}0}_2}}}}{\mathrm{m}}_{\mathrm{i}}$	${\mathrm{V}}_{\mathrm{i}}$ is the acidification potential of component i, with ${\mathrm{V}}_{{\mathrm{S}0}_2}$ specific to SO2. ${\mathrm{M}}_{\mathrm{i}}$ and ${\mathrm{M}}_{{\mathrm{S}0}_2}$ are the mass units for substance i and SO2. ${\mathrm{m}}_{\mathrm{i}}$ is the emitted mass of component i in kilograms.

contains the equations for the toxicological categories.

The use of the WAR algorithm on each stream within the Extractive-based Creole-Antillean Avocado Biorefinery allowed for the identification of those with the highest environmental impacts, paying particular attention to streams involving solvent use and energy-intensive operations, as these typically contribute most to the environmental footprint. The results from the WAR analysis provided valuable insights into potential process modifications, such as improving solvent recovery and implementing energy efficiency measures, to reduce the overall PEI of the biorefinery [17].

3. Results and Discussion

Four different scenarios were created to assess the ecological impact of the Extractive-based Creole-Antillean Avocado Biorefinery, which converts the pulp into bio-oil, the skin into chlorophyll, and the seed into biopesticide. The initial scenario acts as a baseline, concentrating exclusively on the waste without factoring in energy usage or the effects of the produced items. The second scenario includes both the waste and the products, while the third examines the impacts of the waste alongside the energy source, omitting the product effects. The fourth scenario, however, considers the effects of energy consumption, waste, and the outputs from the biorefinery. The comprehensive impact analysis encompassed all four scenarios, with a focus on atmospheric and toxicological categories limited to the fourth scenario, deemed the most critical.

3.1. Overall Impacts of the Extractive-Based Creole-Antillean Avocado Biorefinery

Furthermore, the impacts identified in this evaluation were quantified, offering a clear understanding of the environmental repercussions linked to the avocado biorefinery. Figure 2 illustrates the total PEI generation and the PEI production per ton of bio-oil, chlorophyll, and biopesticide, as well as per hour.

A distinct assessment of the process phases, including the role of waste, is presented in

Table 7. Contribution of process steps to the total hourly PEI output rate considering the effects of residues.

Subprocess	Stage	Output PEI/h	Contribution Considering Residues
Bio-oil	Pulp drying	10	7.84
	Methanol distillation	0.38	0.30
	Bio-oil cooling	4.91	3.85
	Methanol condensation	10.8	8.49
Chlorophyll	Peel drying	0.072	0.06
	Evaporation	6.49	5.09
	Acetone condensation	4.9	3.81
Biopesticide	Seed drying	8.98	7.04
	Fractional distillation	81.0	63.52

. This evaluation aimed to identify the PEI discharges for each stage. As can be seen in Figure 2, for all cases the PEI generation rate is negative (−860, −749, −746, and −746 PEI/h for Cases 1, 2, 3, and 4, respectively). These values suggest that the process reduces environmental impacts by converting a substance with higher environmental consequences into compounds with lower global impact potential, such as bio-oil, chlorophyll, and biopesticides.

It is also evident that Cases 1 and 2 have the lowest output PEI with 125 and 236 PEI/h, respectively, which is related to the non-existence of large amounts of polluting compounds in the overall output streams of the system, considering that there are recirculations. Cases 3 and 4 exhibit slightly elevated exit PEI values, both reaching 238 PEI/h, suggesting that the product significantly contributes to the environmental impacts at the process exit. The findings indicate that energy consumption plays a crucial role in increasing the process’s impact emission rate, more so than the product or waste. Energy consumption is a critical factor not only in environmental assessments but also in economic evaluations, as noted in previous reports [17]. Therefore, optimizing designs to minimize energy usage is of paramount importance.

In addition, Table 7 provides a detailed analysis of the various stages of the process, focusing on the contribution of waste. This analysis aims to identify the output flows of PEI for each stage, with significant production occurring during the pulp drying and fractional distillation stages, yielding 10 and 81 PEI per hour, respectively. These figures are linked to waste generated during avocado washing with sodium hypochlorite, water produced during centrifugation, and steam generated during pulp drying, while the fractional distillation for biopesticide purification results in an ethanol stream that has a higher environmental impact compared to other processes, such as seed drying. Other stages, like size reduction, do not produce waste flows, but only intermediate flows that the WAR algorithm cannot assess, and it is important to note that these values change when energy consumption is considered.

Considering the diagram presented in Figure 3, the pulp drying phase in the bio-oil extraction subprocess accounts for the largest share of total PEI at 66.3%, followed by the acetone evaporation and condensation stages, and the fractional distillation of the biopesticide solvent (ethanol). The pulp drying process operates at a high temperature of 70 °C, leading to increased energy consumption, while the fractional distillation for biopesticide production takes place at 81 °C. Additionally, the process includes size reduction steps that do not require direct energy for heating, resulting in a minimal contribution to PEI; these stages are classified as utilizing energy in the form of work and refrigerants for cooling.

Figure 3 illustrates how various stages contribute to the production of PEI within the process, while also considering the impact of energy consumption. This representation highlights the significance of energy use in each phase and its relationship to the overall output of PEI, providing a clearer understanding of the process dynamics.

Although the current biorefinery design includes basic solvent recovery, it does not yet incorporate integrated energy and mass recovery systems. As such, this model serves as a baseline case for environmental analysis. Without energy recovery processes—such as heat exchange between hot and cold streams or air recirculation in drying stages—the environmental impacts are naturally higher, particularly in the pulp drying stage, which accounts for 66.3% of the total PEI, and in the fractional distillation of the biopesticide. Previous studies have shown that adding thermal integration and recirculation processes in biorefineries can significantly reduce energy use and, as a result, lessen environmental impacts [26,27]. This initial analysis points to key stages where energy integration could make a meaningful difference, suggesting that further technical and economic studies could help confirm the benefits of these improvements.

The literature also highlights the potential of advanced technologies, like bioelectrochemical systems and energy recovery from waste streams, to boost energy efficiency in biorefineries and lower greenhouse gas emissions [26,28]. Integrating these technologies into high-energy stages, such as drying and distillation, could not only cut down environmental impact but also contribute to the sustainability of the process as a whole. For future optimizations, we could consider implementing heat exchangers and air recirculation systems, aligning the process with circular economy goals and resource efficiency in biorefineries [29,30]. By identifying the stages with the highest PEI generation, this study provides a foundation for exploring energy recovery strategies that merit technical and economic evaluation for effective integration.

3.2. Toxicological Impacts of the Biorefinery

Figure 4 illustrates the potential environmental impact associated with the toxicological categories, measured per kilogram of products and per hour, for the biorefinery process. The analysis revealed that negative scores were recorded for the Environmental Impact Points generated in the TTP and HTPI categories. Additionally, the ATP and HTPE categories exhibited low values.

Upon examining Figure 4, it becomes clear that the primary pollution concerns are centered on human toxicity, terrestrial toxicity, and aquatic toxicity. The potential for eutrophication, along with energy consumption and the management of potentially hazardous substances in the biorefinery, such as sodium hypochlorite and ethanol (with LC₅₀ values of 8.14 mg/L and 45.4 mg/L, respectively), may account for these findings [17]. The results indicate that the bio-oil, chlorophyll, and biopesticide production process facilitated the breakdown of organic matter from biorefinery effluents before discharge into the water body [31]. The process’s impacts per unit mass reveal that all categories have values below 1 (−6.45, −0.723, −6.45, and −0.22 for HTPI, HPTE, TTP, and ATP, respectively), with these categories being predominantly influenced by the products and their minimal toxicological effects.

Although the toxicological impacts in this study remain below 1, indicating a low toxicity profile, there is room for improvement through enhanced waste management and integration strategies. Comparatively, Meramo-Hurtado et al. (2020) report higher PEI generation rates in human and terrestrial toxicity categories in their lignocellulosic biomass biorefinery, especially in pathways lacking energy integration and optimized emissions handling [27]. These findings suggest that reducing potentially hazardous substances, such as sodium hypochlorite and ethanol in the biorefinery process, could further decrease toxicological impacts, aligning with the reduced toxicity levels seen in more integrated biorefineries. By implementing additional mitigation measures, similar to those suggested in the Meramo-Hurtado study, even lower toxicological impacts could be achieved, particularly in the categories of human and aquatic toxicity.

3.3. Atmospheric Impacts of the Extractive-Based Creole-Antillean Avocado Biorefinery

The global atmospheric impacts (GWP and ODP) and local impacts (PCOP and AP) related to the production of bio-oil, chlorophyll, and biopesticide from avocados are illustrated in Figure 5. All categories had PEI/kg values under 50. This is because there are few substances released in waste or product streams that contribute to these impacts, with the values mainly reflecting organic compounds like ethanol and energy consumption effects. The PCOP category shows the highest PEI values for both generated and output indicators compared to other atmospheric categories, resulting from the emission of various vapor-phase chemical species due to energy use. In the GWP category, both indicators (produced and generated) were below 1, primarily due to minimal carbon oxide emissions from fossil fuel combustion, specifically natural gas in this study. Likewise, the AP category is affected by the release of gases such as NO_x from natural gas.

A similar approach to reducing atmospheric impacts can be observed in the study on integrating first-, second-, and third-generation biorefineries, where microalgae are incorporated into a sugarcane-based biorefinery. By capturing CO₂ emissions from sugarcane processing and utilizing it to cultivate microalgae, this integration led to a significant reduction in overall CO₂ emissions—approximately 39% lower than in traditional setups [32]. This indicates that incorporating CO₂ management strategies or using alternative energy sources with lower NO_x and carbon oxide emissions could further minimize atmospheric impacts in the production of bio-oil, chlorophyll, and biopesticides from avocados. By adopting such methods, biorefineries may achieve even lower values in categories such as GWP, PCOP, and AP.

3.4. Impacts of the Energy Source

Figure 6 displays the PEI for atmospheric and toxicological categories, depending on the type of fuel utilized to satisfy energy demands. This analysis focuses on Case 4, which accounts for the contributions of the waste, the product, and the energy required to produce the products.

The AP category is notably impacted by energy consumption across all three energy sources, with PEI values ranging between 1.55 and 2.57 PEI/h. This is primarily due to the emission of gases like NO_x and SO_x, which can react with atmospheric water vapor, leading to acidification [33]. As expected, coal exhibits the worst performance, with an acidification potential 0.16 times greater than that of liquid fuels and 0.6 times higher than that of natural gas. This is due to coal’s chemical composition, which contains higher levels of sulfides, nitrogen, and volatile compounds compared to gaseous and liquid fuels. In contrast, the other categories, including GWP, ODP, and PCOP, display nominal emission impacts, with rates under 1 PEI/h.

The toxicological category PEI outputs indicate that energy consumption plays a major role, especially affecting the ATP category. This is largely due to the emission of persistent non-biodegradable volatile organic compounds like benzene and polycyclic aromatic hydrocarbons that remain in aquatic environments. While the TTP, HTPE, and HTPI categories exhibit somewhat lower impacts, they are still influenced by the ATP category because of the presence of volatile gases such as naphthalene and particulate matter. Furthermore, the figure demonstrates that natural gas is superior to other fuels, making it the preferred option for fulfilling the energy requirements of the process.

Supporting these observations, other studies have shown that using sustainable energy sources in biorefineries can greatly reduce atmospheric and toxicological impacts. For instance, incorporating CO₂-capturing microalgae in sugarcane biorefineries has been found to significantly lower GWP values by reducing CO₂ emissions [30]. Similarly, choosing natural gas instead of coal or oil in biodiesel production has been linked to lower emissions of volatile organic compounds (VOCs) and persistent pollutants, such as polycyclic aromatic hydrocarbons, which are key contributors to categories like AP and ATP [34]. In lignocellulosic biorefineries, switching to cleaner energy sources, including natural gas, has also been shown to reduce acidification potential and GWP, aligning with the outcomes observed in this study [27].

4. Conclusions

The environmental assessment of an Extractive-based Creole-Antillean Avocado Biorefinery located in the Montes de María region highlights the substantial influence of energy consumption on the overall environmental footprint, particularly within toxicological and atmospheric impact categories. This study’s application of the Waste Reduction (WAR) Algorithm provided valuable insights into the potential environmental impacts (PEI) associated with various process streams, underscoring the need for careful consideration of energy sources and waste management practices in the biorefinery design. The findings clearly indicate that while the production of bio-based products from avocado waste is feasible, the environmental sustainability of these processes hinges on optimizing energy use and minimizing the release of harmful emissions. The scenario analysis conducted within this study further emphasizes the critical role of energy consumption and product outputs in determining the environmental performance of the biorefinery. Among the scenarios evaluated, those incorporating energy usage demonstrated higher PEI values, particularly in categories like acidification potential (AP) due to the emission of NO_x and SO_x gases. The comparison of energy sources revealed that natural gas presents a more sustainable option compared to coal, suggesting that the careful selection of energy sources is essential for reducing the environmental burden of the biorefinery.

Furthermore, this study identifies key strategies for mitigating the environmental impacts of the biorefinery, including the improvement of solvent recovery processes and the implementation of energy efficiency measures. These strategies are pivotal in addressing the most significant contributors to PEI, particularly in the toxicological and atmospheric impact categories. The research also underscores the importance of integrating sustainability criteria and circular economy principles into the biorefinery’s design and operation to ensure that the environmental benefits of bio-based production are fully realized. Ultimately, this study advocates for the continuous environmental monitoring and assessment of biorefineries as a means of fostering sustainable industrial practices. The integration of tools like the WAR algorithm during the design phase enables stakeholders to identify and mitigate potential environmental impacts early in the process, thus enhancing the overall sustainability of the biorefinery. This proactive approach is not only vital for minimizing the ecological footprint of bio-based production but also aligns with broader efforts to achieve sustainable development goals in the bioeconomy sector.