The inappropriate dumping of agricultural residues (ARs) can result in environmental pollution and the waste of valuable energy resources. The process of converting ARs to energy has been considered an important step for regional energy, agricultural development, and environmental sustainability and recently, many sustainable bioenergy technologies (BETs) have been developed for ARs. Since the assessment of ARs‐to‐energy conversion technologies contains several alternatives concerning multiple criteria with imprecise information, it is deliberated as an uncertain multicriteria decision‐making (MCDM) problem. The Pythagorean fuzzy set (PFS) is an important and effective way to tackle the uncertainty present in real‐life decision‐making problems. To select a suitable conversion technology from a set of options and upgrade the ARs‐to‐energy industries, the present study develops a combined approach to PFSs based on weighted discrimination‐based approximation (WDBA). This method extends the classical WDBA approach using an improved score function and discrimination measure within the PFS context, to evaluate MCDM problems with partial information on the criteria weights. To estimate the weights of the unknown attributes, a score function‐based linear programming model is developed. A new ranking method is extended to grade the options using the proposed discrimination measure within the PFS environment. Further, a case study assessing ARs‐to‐energy conversion technologies is conducted to illustrate the practicality and feasibility of this method. A comparative analysis shows that the approach developed is more effective and proficient in facilitating decision experts' selection of desirable BETs for ARs.

中文翻译：

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基于毕达哥拉斯模糊加权判别的近似方法评估农业残留物的可持续生物能源技术

农业残留物（ARs）的不当倾倒可能导致环境污染和宝贵能源资源的浪费。将ARs转化为能源的过程被认为是区域能源，农业发展和环境可持续性的重要步骤，最近，为ARs开发了许多可持续性生物能源技术（BETs）。由于AR-能量转换技术的评估包含涉及具有不精确信息的多个标准的多种替代方案，因此将其视为不确定的多标准决策（MCDM）问题。毕达哥拉斯的模糊集（PFS）是解决现实决策问题中存在的不确定性的重要且有效的方法。为了从一组选项中选择合适的转换技术并升级ARs到能源行业，本研究开发了一种基于加权判别近似（WDBA）的PFS组合方法。该方法在PFS上下文中使用改进的得分函数和判别措施扩展了经典WDBA方法，以使用有关标准权重的部分信息来评估MCDM问题。为了估计未知属性的权重，开发了基于得分函数的线性规划模型。扩展了一种新的排序方法，以使用PFS环境中建议的区分度对选项进行分级。此外，还进行了一项评估ARs-能量转换技术的案例研究，以说明该方法的实用性和可行性。