source of information is needed to perform parameter estimation, e.g., the experience-based knowledge of experts can be used [9; 10]. Experts are considered in this paper as the workers who operate the technology for a long time, and thus have a significant amount of experience with it, even in extraordinary situations [11]. Thereby, they are supposed to have some instinct about the behavior of the system, and the ability to give some valid prediction about it. However, they are often not able to provide exact information, only a rough estimation. In such cases, the uncertain information can be handled using a probabilistic approach if we have some knowledge about the nature of the uncertainty, or fuzzy sets if we do not have any [12]. Recently, expert knowledge has been shown to be implemented in decision-making using fuzzy logic illustrated by an example of choosing the most suitable solar panel system [13]. On the other hand, the probabilistic approach was used in the risk analysis field in [14], also providing a method for aggregating the subjective assessments of multiple experts. Probabilistically represented expert knowledge is often involved in decision-making [15]; however, it is rarely used in the engineering field. Of course, to compensate for the subjectivity of human opinions, the judgments of multiple experts need to be collected. However, these data from multiple experts are challenging to use appropriately, as these are often partially or completely contradictory due to the subjective assumptions of the information sources [16]. [PERSON] et al. highlight that the inconsistencies are present even considering the judgments of the same expert, and also declare that this can be compensated for and bring about a more consistent form by aggregating all the available knowledge [17]. This can be achieved by using a probabilistic approach [15]. Monte Carlo (MC) simulation is a widely used probabilistic method for handling input or parameter uncertainty, in which probability distributions are represented by a discrete sample. For example, the performance of a solar collector was predicted under uncertain operating conditions [18], the prediction of the cooling load of an HVAC system refined in addition to uncertain inputs [19], and the product yield of a polymerization process with several possible reaction paths estimated in addition to uncertain kinetic parameters using this technique [20]. The importance sampling (IS) technique weights the sample elements by comparing them to reference data [21]. It has been applied, e.g., for the estimation of cosmological parameters based on measurement data [22], and some signal processing case studies can also be seen in [23]. However, these examples all use measurement data for the weighting. Expert knowledge was also sometimes implemented to estimate model parameters; for example, in the case of a groundwater model [24]. However, examples using importance sampling to integrate uncertain expert knowledge and investigate its consistency are rarely documented in the industrial field. The aim of this paper is to fill this gap by providing a methodology combining MC and IS that utilizes uncertain expert knowledge for the parameter estimation of an industrial system. We show how subjective and uncertain information about model parameters and outputs from multiple experts can serve as a basis for estimating these values, involving a probabilistic approach for the representation and investigation of uncertainty. The introduced importance sampling-based method applies to stationary systems; however, a particle filter can be utilized similarly to the above-mentioned for dynamic ones. As another novelty of this study, a technique is provided that shows how results can be used to evaluate the reliability of expert knowledge. Comparing the estimated probability distributions to the original ones provided by experts, the judgments of the experts can be characterized by the goodness values thus evaluated.